Advanced phase shift inspection method

ABSTRACT

A method and apparatus for inspecting patterned transmissive substrates, such as photomasks, for unwanted particles and features occurring on the transmissive as well as pattern defects. A transmissive substrate is illuminated by a laser through an optical system comprised of a laser scanning system, individual transmitted and reflected light collection optics and detectors collect and generate signals representative of the light transmitted and reflected by the substrate. The defect identification of the substrate is performed using only those transmitted and reflected light signals, and other signals derived from them, such as greyscale representations and image features. Defect identification is performed using a pattern inspection algorithm by comparing image feature representations of the present substrate with an idealized representation thereof, and using an advanced phase shift algorithm that accounts for particular types of expected anomalies.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to electro-optical inspectionsystems, and more particularly to a method or algorithm for automatedphotomask inspection to detect defects on optical masks, reticles, andthe like.

2. Description of the Related Art

Integrated circuits are made by photolithographic processes which usephotomasks or reticles and an associated light source to project acircuit image onto a silicon wafer. A high production yield iscontingent on having defect free masks and reticles. Since it isinevitable that defects will occur in the mask, these defects have to befound and repaired prior to using the mask.

Automated mask inspection systems have existed for several years. Theearliest such system, the Bell Telephone Laboratories AMIS system (JohnBruning et al., “An Automated Mask Inspection System—AMIS”, IEEETransactions on Electron Devices, Vol. ED-22, No. 7 July 1971, pp 487 to495), used a laser that scanned the mask. Subsequent systems used alinear sensor to inspect an image projected by the mask using die-to-dieinspection, i.e., inspection of two adjacent dice by comparing them toeach other. Other systems have been developed that teach die-to-databaseinspection, i.e. inspection of the reticle by comparison to the databasefrom which the reticle was made.

As the complexity of integrated circuits has increased, so has thedemand on the inspection process. Both the need for resolving smallerdefects and for inspecting larger areas have resulted in much greaterspeed requirements, in terms of number of picture elements per secondprocessed. The increased demands have given rise to improvementsdescribed in a number of subsequently issued patents, such as U.S. Pat.No. 4,247,203, entitled “Automatic Photomask Inspection System andApparatus”, Levy et al., issued Jan. 27, 1981; U.S. Pat. No. 4,579,455,entitled “Photomask Inspection Apparatus and Method with Improved DefectDetection” Levy et al., issued Apr. 1, 1986; U.S. Pat. No. 4,633,504,entitled “Automatic Photomask Inspection System Having Image EnhancementMeans”, Mark J. Wihl, issued Dec. 30, 1986; and U.S. Pat. No. 4,805,123,entitled “Automatic Photomask Inspection and Reticle Inspection Methodand Apparatus Including Improved Defect Detector and AlignmentSubsystem”, Specht et al., issued Feb. 14, 1989. Also of relevance issome prior art in the wafer inspection area, such as U.S. Pat. No.4,644,172, entitled “Electronic Control of an Automatic Wafer InspectionSystem”, Sandland et al., issued Feb. 17, 1987.

Another force driving the development of improved inspection techniquesis the emergence of phase shift mask technology. With this technology,it becomes possible to print finer line widths, down to 0.25 micrometersor less. This technology is described by Burn J. Lin, “Phase-Shiftingand Other Challenges in Optical Mask Technology”, Proceedings of the10th Annual Symposium on Microlithography, SPIE,—the InternationalSociety of Optical Engineering, Vol. 1496, pages 54 to 79.

The above improvements teach the automatic detection of defects onconventional optical masks and reticles. In all of these systems,conventional lighting is used and the images are captured by lineararray sensors. These two system choices limit the signal-to-noise ratioand hence the speed of inspection.

Additionally, photomasks are used in the semiconductor manufacturingindustry for the purpose of transferring photolithographic patterns ontoa substrate such as silicon, gallium arsenide, or the like during themanufacture of integrated circuits. The photomask is typically composedof a polished transparent substrate, such as a fused quartz plate, onwhich a thin patterned opaque layer, consisting of figures, has beendeposited on one surface. The patterned opaque layer is typicallychromium with a thickness of 800 to 1200 angstroms. This layer may havea light anti-reflection coating deposited on one or both surfaces of thechromium. In order to produce functioning integrated circuits at a highyield rate, the photomasks must be free of defects. A defect is definedhere as any unintended modification to the intended photolithographicpattern caused during the manufacture of the photomask or as a result ofthe use of the photomask. Defects can be due to a variety ofcircumstances, including but not limited to, a portion of the opaquelayer being absent from an area of the photolithographic pattern whereit is intended to be present, a portion of the opaque layer beingpresent in an area of the photolithographic pattern where it is notintended to be, chemical stains or residues from the photomaskmanufacturing processes which cause an unintended localized modificationof the light transmission property of the photomask, particulatecontaminates such as dust, resist flakes, skin flakes, erosion of thephotolithographic pattern due to electrostatic discharge, artifacts inthe photomask substrate such as pits, scratches, and striations, andlocalized light transmission errors in the substrate or opaque layer.During the manufacture of photomasks, automated inspection of thephotomask is performed in order to ensure freedom from theaforementioned defects.

There are, at present, three general methods for the inspection ofpatterned masks or reticles. One of those inspection methods is adie-to-die comparison which uses transmitted light to compare either twoadjacent dies or a die to the CAD database of that die. Thesecomparison-type inspection systems are quite expensive because they relyon pixel-by-pixel comparison of all the dies and, by necessity, rely onhighly accurate methods of alignment between the two dies used at anyone time for the comparison. Apart from their high costs, this method ofinspection is also unable to detect particles on opaque parts of thereticle which have the tendency to subsequently migrate to parts thatare transparent and then cause a defect on the wafer. One suchdie-to-die comparison method of inspection is described in U.S. Pat.Nos. 4,247,203 and 4,579,455, both by Levy et al.

The second method for inspecting patterned masks is restricted tolocating particulate matter on the mask. It makes use of the fact thatlight scatters when it strikes a particle. Unfortunately, the edges ofthe pattern also cause scattering and for that reason these systems areunreliable for the detection of particles smaller than one micrometer.Such systems are described in a paper entitled “Automatic. Inspection ofContaminates on Reticles” by Masataka Shiba et al., SPIE Vol. 470Optical Microlithography III, pages 233-240 (1984).

A third example of a system for performing photomask inspection isdisclosed in U.S. Pat. No. 5,563,702 to David G. Emery, issued Oct. 8,1996. The system disclosed therein acquires reflected images, inaddition to transmitted images, to locate defects associated withcontaminants, particles, films, or other unwanted materials. Since thissystem locates defects without reference or comparison to a descriptionor image of the desired photomask pattern, it does not locate defectsassociated with photomask pattern errors, dislocations, orirregularities.

It has further been found to be advantageous to acquire both transmittedand reflected images for inspection of a photomask pattern with adie-to-die or die-to-database system. In particular, this approach hasbenefits for Embedded Phase Shift Mask (EPSM) inspection and AlternatingPhase Shift Mask (APSM) inspection. Transmitted EPSM images oftencontain unfavorable optical characteristics due to partial coherence andinterference induced by phase shifting. Some EPSM defects are simplyeither undetectable or indiscernible using existing die-to-die ordie-to-database systems with transmitted images, but are neverthelessundesirable. Acquisition of both transmitted and reflected images forEPSM inspection has.

APSMs are typically designed with thickness variations in the glass orquartz which induce phase shift transitions between adjacentn regionsduring photolithography. Phase defects can exist which are unwantedthickness variations created by phase etch process errors, and havesimilar optical image signatures during inspection. Hence APSM phasedefects are difficult to distinguish from design phase features usingthe system shown in U.S. Pat. No. 5,563,702. Phase defects cannot bedetected by this system without producing false defect readings on phaseshift design features where no defects actually exist. However, thetransmitted and reflected imaging capabilities and defect detectionoperators of this system can be useful to determine the presence ofphase defects if all detected phase features are properly compared andcontrasted to reference photomask image data, as in a die-to-die ordie-to-database system.

Furthermore, the phase defect signal in brightfield transmitted lightcan be substantially less than that of a similarly sized chrome defect,thereby complicating the ability to inspect the mask. The phase defect'ssignal depends on a variety of factors, including the height or phaseangle of the phase defect, the depth of the phase shifters, andinspection system optical parameters.

The use of reflected light in combination with transmitted light mayimprove detection of phase defects. The difficulty with using reflectedlight is managing image artifacts, such as the bright chrome halosresulting from the removal of the antireflective chrome layer duringquartz etching of phase shifters. Bright chrome halos may have variablewidths resulting from second write level registration tolerances withintra-plate variations. These variations are not observable when solelyusing transmitted light inspection techniques.

Thus, in general, phase feature signals captured with brightfieldtransmitted light may vary widely depending on mask and defectcharacteristics, and phase feature signals captured in reflected lightmay be stronger. On the other hand, use of reflected light can beproblematic in the presence of image artifacts such as bright chromehalos. Therefore, die-to-die or die-to-database photomask inspectionwith transmitted and reflected light may benefit from signal-to-noiseenhancements as well as an enhanced ability to discern phase shiftfeatures and phase defects.

Some references have suggested inspecting photomask substrates utilizingboth transmitted and reflected light and have mentioned the possible useof both to classify defects. However, none of these references haveprovided details on a design using transmitted and reflected light tolocate photomask pattern defects, and none have addressed the problemsassociated with the use of reflected light in the presence of brightchrome halos, or in the presence of particular anomalies when inspectingusing brightfield transmitted light.

SUMMARY OF THE INVENTION

A preferred embodiment of the present invention includes an X-Y stage(12) for transporting a substrate (14) under test in a serpentine pathin an X-Y plane, an optical system (16) including a laser (30), atransmission light detector (34), a reflected light detector (36),optical elements defining reference beam paths and illuminating beampaths between the laser, the substrate and the detectors and anacousto-optical beam scanner (40, 42) for reciprocatingly scanning theilluminating and reference beams relative to the substrate surface, andan electronic control, analysis and display system for controlling theoperation of the stage and optical system and for interpreting andstoring the signals output by the detectors. The apparatus can operatein a die-to-die comparison mode or a die-to-database mode.

In the present invention the speed is further enhanced by the use of adeflection apparatus previously described for laser beam recording byU.S. Pat. No. 3,851,951 to Jason H. Eveleth, entitled “High ResolutionLaser Beam Recorder with Self-Focusing Acousto-Optic Scanner”, issuedDec. 3, 1974.

Another advantage is the use of a stage that has only two degrees offreedom. Prior systems incorporated rotational capability at aconsiderable cost and complexity. In the present invention the effectivedirection of scanning is controlled by driving both axes of the stagesimultaneously.

The present system also has the ability to simultaneously detect defectswith both transmitted and reflected light. This capability issignificant because the additional information can be helpful indetermining the nature of the defect and thereby permits the automaticclassification of defects.

Yet another advantage of the first aspect of the present invention isits ability to inspect phase shift masks. Phase shift mask technologymay be used to achieve line widths of 0.10 micrometers. In the presentinvention the phase shift material can be measured at all points on amask area at the normal scanning speed of the system.

In accordance with the present invention there is provided a novelmethod and apparatus for the inspection of photomasks at a highsensitivity to detect submicron particulate contamination, chemicalstains and residues, and localized transmission variations by utilizingsynchronized transmitted and reflected light signals (i.e. from the samelocation on the substrate with either the same light beam or two lightbeams of equal intensity and cross sectional size and shape illuminatingthe same location on the substrate).

Further there is provided a pattern inspection algorithm on both thetransmitted and reflected images to determine defects at and around theedges of the specimen pattern. The system simultaneously samplestransmitted and reflected images and passes the data to a remappingblock which converts each T-R image sample to a single output greyscalevalue. The remap function is designed to produce images with correctedoptical characteristics by reference to reflected greyscale data, whichis not altered by transmissive phase-shifting. The system performs apattern inspection algorithm on the remapped image to determine defectsat and around the specimen pattern. By remapping the transmitted andreflected images into a single image, the processing requirements forpreprocessing, alignment, interpolation, and comparison need not beduplicated for both images. The remap correction requires an analysis ofthe correlated relationships between transmitted and reflected greyscalevalues.

The remap function is determined before inspection during a calibrationprocedure by evaluating samples of representative transmitted andreflected images. The calibration can be performed by various methods,where the common objective is to analyze the correlation betweentransmitted and reflected values and assign an appropriate relationshipbetween transmitted and reflected input values and remap output values.For any method, remap calibration must function effectively in thepresence of greyscale measurement noise.

To allow for noise, off-curve points may be parameterized by selectingthe nearest neighbor on the curve. After the entire TR-plane iscompletely parameterized, the remap function is then stored into theremapping block for reference during inspection.

Subsequent to the initial calibration procedure, the system scans thedesired specimen to inspect it for defects. The system remaps the TRreadings into single greyscale values thereby permitting a combinationof transmitted and reflected images into a single intensity profile. Thesystem may include filters networks to improve detectability.

The remapped optical image is further processed in a transform block inpreparation for alignment and comparison with a pattern reference image.If the reference image is derived from a database, the database image isalso processed in a transform block. Data from both the opticaltransform block and the database transform block are provided to thealign, interpolate, and compare block which evaluates the distance codevalues from the database and from the scanned specimen and determinesdifferences between the values.

One aspect of the present invention is based upon a laser scanner,optical conditioning subsystem, a stage, reflectance and transmissiondetectors, and an autofocus subsystem as disclosed in the abovecross-referenced Wihl patent application.

The system further includes a transmitted and reflected light algorithmor method for improving detection of particular types of featuresexpected to be encountered on the mask. The algorithm provides thesystem with the ability to differentiate and resolve anomalies found onstrong phase shift type masks, including alternating and darkfieldalternating phase shift mask types. Further, the algorithm provides theability to manage image artifacts such as bright chrome halos. Thealgorithm determines the remap function S=S(T,R), utilizing differentspecies employing different analytical approaches to derive the remap.The species are each designed for a specific anticipated application,and thus the algorithm operates as an expert system to locateanticipated anomalies.

These and other objects and advantages of all of the aspects of thepresent invention will become apparent to those skilled in the art afterhaving read the following detailed disclosure of the preferredembodiments illustrated in the following drawings.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified functional block diagram of a laser maskinspection system in accordance with the first aspect of the presentinvention.

FIG. 2 is a more detailed schematic representation of the opticalsubsystem depicted in FIG. 1.

FIG. 3 is a diagram illustrating the scanning path used in thedie-to-die inspection mode.

FIG. 4 is a diagram illustrating the scanning path used indie-to-database inspection mode.

FIGS. 5 and 6 are diagrams illustrating possible beam cross sectionsused in the autofocus system.

FIG. 7 is a partially broken perspective drawing illustrating the X-Ystage.

FIG. 8 is a cross-section taken along the line 8-8 of FIG. 7 showingdetails of the construction frame of the stage.

FIG. 9 is a cross-section taken along the line 9-9 of FIG. 7 showingother details of the construction frame of the stage.

FIG. 10 is an illustration of a cross-section of a typical phase-shiftmask showing in exaggerated scale an illustration of the phase-shiftedoutput of the reflected beam detector.

FIG. 11 is an illustration of the sinusoidally varying detected signalintensity as the mask is scanned in phase shift measurement mode.

FIG. 12 is a block diagram depicting a phase-locked loop subsystem usedto detect phase-shift material thickness.

FIGS. 13 a and 13 b are simplified schematic diagrams respectivelydepicting operation of the optical subsystem used for measuring thephase-shift material thickness in the transmitted and reflected lightmodes.

FIG. 14 is an illustration used to describe the method of line widthmeasurement.

FIG. 15 is a simplified functional block diagram of the laser maskinspection system of the second aspect of the present invention and amodification of FIG. 1.

FIG. 16 is a more detailed schematic representation of the opticalsubsystem depicted in FIG. 15 and a modification of FIG. 2.

FIG. 17 is a normalized plot of the transmitted light and reflectedlight signals detected by sensors of the second aspect of the presentinvention for one scan of the laser scanner.

FIG. 18 is a normalized plot of the transmitted, reflected, andsummation signals for a optical subsystem of the second aspect of thepresent invention showing the effect of particulate contamination onthose signals.

FIG. 19 is a graph of the relationship between transmitted and reflectedlight signal pairs in the absence of defects as per the second aspect ofthe present invention.

FIG. 20 is a graph as in FIG. 19 which shows the additional loci ofpoints resulting from particulate contamination on the opaque layer, atthe edge of a feature, and on the photomask substrate as per the secondaspect of the present invention.

FIG. 21 is a graph of transmitted light values versus the secondderivative transmitted light values as per the second aspect of thepresent invention.

FIG. 22 is a graph of reflected light values versus the secondderivative reflected light values as per the second aspect of thepresent invention.

FIG. 23 a is a pixelized transmission image of the substrate beinginspected.

FIG. 23 b is a pixelized second derivative transmission image, derivedfrom the pixelized transmission image of the substrate being inspected.

FIG. 24 is a block diagram shown in three tiers with the transmissionand reflection observed pixel maps as input signals which are operatedon by a selected number of different filters, the filter output signalsbeing combined pair-wise in the second layer, and the third layerproviding a merge function to identify all of the defects detected fromeach of the pair-wise signal combinations of the second layer.

FIG. 25 is a typical representation of a BPN neural network.

FIG. 26 shows a graph of the relationship between transmitted andreflected light signal pairs when inspecting typical embedded phaseshift mask.

FIG. 27 a is an illustration of the prior system for detecting defects.

FIG. 27 b illustrates the application of the image processing operationsshown in FIGS. 19-24 on both transmitted and reflected images to produceoptical image features.

FIG. 28 presents the current system for performing inspection of anembedded phase shift mask using a pattern inspection algorithm.

FIG. 29 represents a T-R curve for the present system.

FIG. 30 illustrates a simplified version of the system incorporating theadvanced phase shift (APS) method or algorithm.

FIG. 31 is a diagram of the APSM species based on transmitted (T) andreflected (R) light received.

FIG. 32 is a diagram of the TT1 species based on transmitted (T) andreflected (R) light received.

FIG. 33 is a diagram of the TT2 species based on transmitted (T) andreflected (R) light received.

FIG. 34 is a diagram of the TT3 species based on transmitted (T) andreflected (R) light received.

FIG. 35 is an alternate diagram of the TT3 species based on transmitted(T) and reflected (R) light received.

DETAILED DESCRIPTION OF THE INVENTION

Referring now to the drawings, a block diagram of an automatic opticalinspection system in accordance with the first aspect of the system isshown at 10. The system is capable of inspecting substrates, such asreticles, photomasks, semiconductor wafers, phase shift masks, andEmbedded Phase Shift Masks (EPSMs).

The system can perform several types of inspection: transmitted lightinspection, reflected light inspection, simultaneous reflected andtransmitted inspection, and phase shift measurement. In transmittedlight inspection, light impinges on the substrate, a photomask forexample, and the amount of light transmitted through the mask isdetected. In reflected light inspection, the light reflecting from asurface of the substrate under test is measured. During phase shiftinspection, the amount of phase shift between two reflected coherentlight beams is detected at each point on the mask while transmittedlight inspection takes place concurrently. The phase shift isproportional to the relative elevation of the surface from which thebeams are reflected. As will be explained below, the transmitted lightsignal is used to qualify the phase-shift signal. In addition to thesedefect detection operations, the system is also capable of performingline width measurement.

In all of the defect detection operations a comparison is made betweentwo images. In die-to-die inspection mode two areas of the substratehaving identical features (dice) are compared with respect to each otherand any substantial discrepancy is flagged as a defect. In thedie-to-database inspection mode a defect is detected by comparing thedie under test with corresponding graphics information obtained from theCADS (computer aided database system) database from which the die wasderived. In the latter case the CADS database is converted to an imageformat as explained in U.S. Pat. No. 4,926,489. (Danielson at al.,“Reticle Inspection System”, issued May 15, 1990).

As depicted in the simplified block diagram of FIG. 1, a preferredembodiment of the system 10 includes a stage 12 for carrying a substrate14 to be inspected, an optical subsystem 16, a data base adaptor 18, anelectronics subsystem 20, a display 22, a control computer 24 and akeyboard 26.

The Stage

Although a preferred embodiment of the stage 12 will be described indetail below, it suffices at this point to say that the stage is aprecision device driver under control of subsystem 20 and is capable ofmoving the substrate 12 under test in a serpentine fashion, within asingle plane, relative to the optical axes of the optical subsystem 16so that all or any selected part of the substrate surface may beinspected.

Optical Subsystem

A detailed block diagram of the optical subsystem 16 is shown in FIG. 2and is essentially a laser scanner apparatus including a light source302 and associated optics which cause a beam 32 of coherent light to bedeflected over a small angle, i.e., from one side to the opposite sideof the optical axis defined by the optical subsystem 16. As will befurther described below, the beam sweep is in a direction such that,after passing through the optical system, it is directed parallel to theY-axis as viewed at the substrate 14. As the beam is swept, the stage 12carrying the substrate 14 under test is caused to move back and forth inthe direction of the X-axis, being incremented in the Y-direction at theend of each traverse so that the beam 32 is caused to sweep along aserpentine path 31 across a plurality of identified substrate subareas33, 35, 37 (individual dice in the case of a photomask) as indicated inFIGS. 3 and 4. In this manner the entire surface area of the substrate(mask) 14 is swept in a series of contiguous swaths 39 by the laserbeam. In the case of a transparent or partially transparent substrate,detection of the image is accomplished by a transmission detector 34. Inthe case of a reflective or partially reflective substrate, the lightreflected from the substrate is detected by a reflected light detector36. As will be explained in more detail later, phase shift maskinspection is carried out by using both of these detectorssimultaneously.

The light source 30 of the system is a laser, such as the Model5490A5L-00C-115 made by Ion Laser Technology of Salt Lake City, Utah.The light beam 32, emitted by the laser 30, first passes through aspatial filter 38 and is then deflected by the combination of twoacousto optic elements; an acousto-optic prescanner 40 and anacousto-optic scanner 42. These two elements deflect the light beam inthe Y-direction and focus it in the X-direction in a manner similar tothat described in U.S. Pat. No. 3,851,951. (Jason H. Eveleth, “HighResolution Laser Beam Recorder with Self-focusing Acousto-opticScanner”, issued Dec. 3, 1974). The deflection system also includes abeam expander 44 and a quarter wave plate 46.

When the beam emerges from the scanner 42, it is convergent in theY-direction, but collimated in the X-direction. A cylindrical lens 50then also focuses the beam in the X-direction, with the focal plane forboth X and Y axes lying at a field stop 52. The beam next passes througha quarter wave plate 54 and a relay lens 56.

The beam is then reflected by a mirror 58, the sole function of which isto fold the optical path. The redirected beam then enters a cube beamsplitter 60 which divides it into paths 62 and 64. The latter path isused only in the phase measurement mode and is otherwise blocked by ashutter 63.

The beam continuing along path 62 is reflected by an oscillating mirror65 which is held fixed during the inspection operation and is used onlyfor displaying an image to an operator on an image display (not shown inFIG. 2) during alignment and review. A dove prism 66 is used to rotatethe direction of the scan about the optical axis. The output of prism 66is fed to one of the telescopes 68 and 70 mounted on a rotatable turret72. The purpose of these telescopes is to vary the size of the scanningspot on the substrate 14 and thereby allow selection of the minimumdetectable defect size. Since changing the magnification also varies thelength of the scan, the swath width is also changed and therefore theinspection speed. (Only two telescopes are shown but obviously anynumber of telescopes, and therefore spot sizes, can be used.)

From the telescope the beam passes to a mirror 74 and then to a beamsplitter 76 where the path is again split. The reflected portion of beam78 is directed to a detector 80 which serves as a monitor of the beamintensity variation. The unreflected portion of the beam passes throughan objective lens 82 which focuses the beam onto the substrate 14. Lightpassing through the substrate 14 is then collected by a condenser lens84 and a collector lens 86, and focused onto the transmission detector34.

Autofocus Subsystem

The autofocus function is based upon a monitoring of the shape of thelight beam cross-section after it is passed through some anamorphicelements. The basic principle underlying the implementation is that acylindrical lens produces astigmatism. In such a case a focussed beamfirst passes through best focus in one direction and then through bestfocus in the perpendicular direction. In between these two focal pointsalong the beam path the beam cross section is oblong in one directionand transitions along the path through points where the beam crosssection is circular and then oblong in a direction perpendicular to theprevious direction. In this invention the optimum focus of the lightimpinging on the substrate is detected by monitoring the beam crosssection of light reflected from the substrate 14. The shape of the beamcross section is monitored by two silicon quadrature photodiodes 90 and92, such as made by Silicon Detector Corporation of Newbury Park, Calif.

As is explained in more detail below, the actual autofocus systemconsists of two optical paths which differ from each other in thedirection of the astigmation. In one path the cylindrical lens has nocurvature when viewed in the X-direction while in the other path, thecylindrical lens has no curvature in the Y-direction.

The autofocus beam 93 is split off from the reflected beam 95 directedalong reflected detector path by a beam splitter 94, and is redirectedtoward another beam splitter 96 which splits the beam into two paths 98and 100. In FIG. 2 the x-coordinate is perpendicular to the paper andconsequently, cylindrical lens 102 is shown with a curvature, while anidentical element 104, in the other path, appears as a plano-parallelelement. The path leading to detector 90 also contains a spherical lens,106. The two identical quadrature detectors 90 and 92 detect across-section of each beam. As the substrate surface position, orthickness, varies, the beam cross section, as seen by the detectors,varies in the X-direction as shown in FIGS. 5 and 6 at 108, 110 and 108,112 respectively. On neither detector does the vertical (Y-direction)diameter of the illuminated area change. When the mask is in focus, bothdetectors are illuminated by a circular beam 108. As the mask goes outof focus, the horizontal diameter shrinks on one detector (see FIG. 5),while on the other one it increases (see FIG. 6) as indicated by theoutlines of the beam 110 and 112, respectively. This changes theelectrical output from the quadrature detectors. The focus correctionsignal F_(c) is then:$F_{c} = \frac{\left( {A_{1} - B_{1}} \right) - \left( {A_{2} - B_{2}} \right)}{\left( {A_{1} + B_{1}} \right) + \left( {A_{2} + B_{2}} \right)}$where A₁ is the signal derived from quadrants along the X axis of 90,

-   -   A₂ is the signal derived from quadrants along the X axis of 92,    -   B₁ is the signal derived from quadrants along the Y axis of 90,    -   B₂ is the signal derived from quadrants along the Y axis of 92.        Transmitted Light Inspection Mode

Ordinarily, transmission mode detection is used for defect detection onsubstrates such as conventional optical masks having transparent areasand opaque areas. As the laser beam scans the mask, the light penetratesthe mask at transparent points and is detected by transmitted lightdetector 34 which is located behind the mask 14 and measures the lightcollected by condenser lens 84 and collector lens 86.

Reflected Light Inspection Mode

Reflected light inspection is normally performed on opaque substratesthat contain image information in the form of developed photoresistfeatures. Light reflected by the substrate passes backwards along thesame optical path as described before but is then diverted by apolarizing beam splitter 60 into detector 36. A condenser lens 35projects the light onto the detector 36. As previously stated, duringreflected light inspection, shutter 63 is closed.

Reflected light inspection may also be used to detect contamination ontop of opaque substrate surfaces.

Phase Shift Material Thickness Measurement Mode

The measurement of phase shift is of interest only at points where thesubstrate is transparent, i.e., where there is no opaque geometry. Thepresence of opaque geometry is detected by the transmission detector 34and only in the spaces separating such geometry is a phase shiftmeasurement taken. During this operation shutter 63 is open and lightfrom the laser reflected by splitter 60 travels through relay lenses 110and 112, which form a telescope 114, and through a low numericalaperture objective lens 116 to a tilted mirror 118 where it is reflectedby detector 36. At the same time, detector 36 is also illuminated bylight which first passes through splitter 60 to be reflected from apoint on the substrate and which on returning is reflected by splitter60 to the detector. These two luminous beams interfere with each other,and the intensity of the light detected by detector 36 therefore variesas a function of the relative optical path length of the two paths 62and 64. As will be explained in more detail later, this data isinterpreted by the electronic subsystem to determine variations ofthickness of phase shift material covering a given point on thesubstrate.

Simultaneous Detection by More than One Type of Detector

Transmitted and reflected light inspections and the phase shiftmeasurement operation are not mutually exclusive in time. Simultaneoustransmitted and reflected detection can disclose the existence of anopaque defect sensed by the transmitted detector while the output of thereflected detector can be used to disclose the type of defect. As anexample, either a chrome dot or a particle is opaque and hence willresult in a dark output from the transmission detector, but reflectivechrome defects also produce a high reflected light indication while aparticle will typically reflect less. By using both reflected andtransmitted detection one may locate a particle on top of chromegeometry. In general, one may determine signatures for certain types ofdefects, such as the ratio of their reflected and transmitted lightintensities. This information can then be used to automatically classifydefects.

Similarly, transmitted light detection and phase shift measurement canoccur simultaneously. On a phase shift mask an opaque defect in a regioncovered by phase-shift material can be detected, and the absence ofopaque material detected by the transmitted light detector 34 can beused to gate the phase shift measurement.

Control Computer

The control computer 24 acts as the operator console and mastercontroller of the system and is a device such as a SPARC computer madeby Sun Microsystems of Mountain View, Calif. All system interfaces withthe operator and the user's facilities are made through the controlcomputer. Commands are issued to and status is monitored from all othersubsystems so as to facilitate completion of the operator assignedtasks.

Electronics Subsystem

The function of the electronics subsystem 20 is to interpret and executethe commands issued by control computer 24. These functions are:digitize the input from detectors 34 and 36; compensate these readingsfor variations in the incident light intensity; detect defects in theimage and transfer the defect data to the control computer 24;accumulate the output of the interferometers used to track the stage 12;provide the drive for the stages linear motors; and monitor sensorswhich indicate status.

Except for the measurement of phase shift and line width information,all of the enumerated functions of control computer 24 and subsystem 20have been described in U.S. Pat. Nos. 4,247,203, 4,579,455, 4,633,504,4,805,123, 4,926,489, and 4,644,172. In the above patents the samefunctions are performed in many different ways and the particularapproach adopted depended on the availability and suitability ofintegrated circuit devices at the time the system was being developed.Any of the cited approaches could be used.

The Stage

The stage 18 is an air-bearing X-Y stage that is driven by a linearmotor on each axis. The position of the stage along each axis ismonitored by interferometers (not shown), such as the Model TIPS V, madeby Teletrac Corporation.

Stage 18 is shown in detail in FIG. 7 with the front rail cut away topermit view of the principle elements. The stage has two degrees offreedom; it has no rotational capability. It is disclosed here forapplication in the described inspection system but could also be used inmicrolithography and any precision machining application.

The Y carriage 120, in the shape of a frame 122, carries the X stage124. The motion of both stages is controlled by linear motors and airbearings. The attractive force between the stator and the slider of eachlinear motor provides the preload of the linear bearings.

The Y carriage frame includes two guideways 126 and 127, controlling themotion of the X stage 124 inside the carriage. The guideways areconnected by two side rails 128. (The front rail, the equivalent of 128,is not shown.) The stator 129 of the X linear motor is imbedded insidethe X guideway 126 in such a way that it attracts the X slider 130attached to air-bearing housings 131 and preloads four of the five X airbearings 132, 133, 134 and 135. A separate magnet 136 and ferromagneticpreload strip 137 provide the preload to air bearing 138. Each bearingis equipped with a swivel, enabling rotation of the bearing pad abouttwo axes, in addition to rotating the bearing itself, thus the onlydegree of freedom constrained by an air bearing is the translation inthe direction normal to the pad surface.

The X stage carries the specimen 14 and is kinematically constrained bythe five air bearings: the bearings 132 and 135 control the pitch of theX stage motion, and constrain the vertical translation in the Zdirection, bearings 133 and 134 control the yaw of the X motion andconstrain the horizontal translation in the Y direction. Bearing 138nested in the housing 139 controls the roll of the X stage andconstrains vertical translation of the stage in the Z direction. Thespecimen holder assembly 140 is attached to a lightweight compositeframe 141 of the X stage.

The stage contains a number of novel features. One such feature is theuse of the linear motor to preload the stage in two directions andthereby achieve an exceptional stiffness. This is accomplished by thearrangement of triangular cross section slider iron 130 and angularposition of the stator 131, so that the magnetic attraction force is atan angle to all four air bearings 132, 133, 134 and 135.

Another feature of the design is that the stator 129 of linear motor isimbedded inside the guideway 126 at an angle to the two walls of theguideway.

Also novel is the use of honeycomb material, such as Blue Seal, made byHexcell of Dublin, Calif., for the construction of frame 140. Thisreduces the mass of the stage, yet makes it very rigid. A cross-sectionof this construction taken along the line 8-8 is shown in FIG. 8 wherecellular insert 142 is sandwiched between skins 143. The bottom plate144 and top plate 145 join the skins 143 and complete the box structureenclosing the insert 142. The honeycomb material may be replaced by anynumber of light composite materials, such as Duocell, manufactured byERG of Oakland, Calif.

Solid pieces 146 are attached to the composite such that they penetrateone skin of the composite wall and are attached to the opposite skin andeither of the top or bottom plates, as shown in FIG. 9, with joints 147formed around the penetration through the wall, and between the solidpiece and the inside of the opposite skin and the plate 144.

Operation of the Disclosed Embodiment

Alignment

Prior to starting the automatic inspection operation, the operatoraligns the mask in the proper orientation and defines to the computerthe “care area”, i.e., the area to be inspected. FIG. 3 illustrates thedesired orientation of the inspection path 31 with respect to dice 33,35, and 37 shown here on a multi-die mask or reticle 14. Duringinspection, the stage 12 is moved in a serpentine manner, following thepath 31, while the laser beam is deflected parallel to the Y-axis of themask. As stage 12 moves in the X-direction, this Y-axis motion of thelaser beam sweeps out a swath, 39. Ordinarily the axes of mask 14 willnot be parallel to the drive axis of the stage. Therefore, an X or a Ydirectional motion of the stage requires both of the drives of the stageto be driven simultaneously. The first task of the operator is thereforeto define to the system the ratio of the speeds of the major axes of thestage. To accomplish this, the operator chooses two points known to himto lie on the same X-coordinate of the die. He then drives the stage tothese points, while observing the image on image display 22. The systemnotes the location of these points by measuring the travel withinterferometers (not shown) along the drive axes of the stage. Thesemeasurements establish the direction cosines of the stage drive axeswith respect to the X and Y axes of the mask. At this time the doveprism 66 (FIG. 2) is rotated to orient the deflection of the laser beamso that it is perpendicular to the X-directional travel of the stage.Next, the operator designates to the system the care area 41 (FIG. 3) ofthe die, the area to be inspected.

Phase Shift Measurement Calibration

For reasons that will become apparent later, in the phase measurementmode, as the laser spot scans (in the Y-direction), a flat transparentsurface parallel to the plane of the mask, the intensity variessinusoidally, as shown by curve 200 in FIG. 11. Mathematically, theintensity I is:I=A sin[(2Πy/w)−D)]+I _(o).where y is the distance of the pixel in question from the origin, w is aconstant that is a function of the tilt angle of mirror 118, D is thephase shift due to path length change as the result of the thickness ofthe phase shift material, A is the half-amplitude of the intensity, andI_(o) is the intensity offset 204 due to stray light in the optics.These values are all determined during the phase shift measurementcalibration part of the initialization. As the laser scans a flatuniform transparent area of the mask, the intensities at each pictureelement (pixel) are digitized and stored in the computer. Then, I_(o) isthe average value of the intensities over integer cycles, and A can becomputed from:A=(I _(max) −I _(o))/2The value W is the periodicity of the sinusoid.

I_(o) and A are different for clear and phase shift material coveredareas and therefore must be determined for both areas. The quantity D isa linear function of the thickness of the phase shift material and thisrelationship is determined by calibration on a known sample containingvarious thickness phase shift material features and remains constantwhile the system retains dimensional stability.

The Inspection Process

Automatic inspection of a reticle ordinarily starts at the upper lefthand corner of the care area and follows the serpentine pattern 31 (seeFIG. 3). As the stage slowly moves in the X direction, the laser beamrapidly sweeps in the Y-direction. In this manner a swath 39 is scannedand the digitized output of the detectors is stored in the electronicssubsystem 20. When the swath reaches the left boundary of the care areaof the second die 35, image data derived from die 33, and now stored insubsystem 20, is compared with the data derived from die 35. Anysubstantial difference is designated a defect. In a similar manner, thedata from die 37 is also compared with the data derived from die 35.

When the scanning process reaches the right boundary of the care area ofdie 37, the stage is moved in the Y-direction an amount slightly lessthan the swath width and the stage starts a return trace in theX-direction. In this manner the care areas of the dice are traversed bythe serpentine motion.

Die-to-database inspection, ordinarily performed on single die reticles,is similar to die-to-die inspection except that the comparison occursbetween the die and a simulated image generated by database adaptor 18.FIG. 4 illustrates the die-to-database scan path 31′.

Review Operation

After completion of the automatic inspection operations, the operatorreviews the defects by causing control computer 24 to move the stage 12to the area of a particular defect and hold it there. The image is thenscanned by acousto-optic scanners 40 and 42 in the Y-direction and byoscillating mirror 65 in the X-direction, and the digitized image isdisplayed on display 22. The operator may use the output of any of thedetectors or the combination of outputs from more than one detector. Ifthe operator desires, the different detector outputs may be superimposedand represented as separate colors on the display.

Phase Shift Material Thickness Measurement

FIG. 10 is an illustration of the cross section of one type of a phaseshift mask. While the present example relates to a particular type ofmask, on all types of masks, control of the thickness of phase shiftmaterial is a requirement and hence the technique described here isapplicable to all types of phase shift masks.

The substrate 160 is typically of quartz on which opaque features 164are deposited. These are typically thin layers of chrome. Phase shiftfeatures 161 and 162 made of transparent material will typicallypartially overlay part of the chrome 164 and some of the clear areas 181and 183 between the features 164. Phase shift material filledtransparent areas 181, 183 and clear areas 180, 184 typically alternate.The height of the upper surface 173 of the phase shift feature 162 abovethe level of the front, or upper, surface 174 of the quartz substrate istypically such that it produces a phase shift of 180 degrees withrespect to a point 180 in the same plane but not covered by phase shiftmaterial.

Defects in phase shift masks may occur in several ways. There can bedefects in the transparent areas, such as either excess chrome or dirt,or there can be missing chrome in a feature 164. Such defects aredetected by the transmitted light detector 34 (FIG. 2) and are thesubject of previously referenced publications. Defects may also bedetected in the phase shift layer 161 or 162. There are two types ofdefects: those where there is a sudden variation of thickness of thephase shift layer, and those in which there is a deviation from thedesired thickness which is either constant, or varies slowly over thephase shift layer. This occurs as the divot 168 in layer 161 is detectedby the transmitted light detector 34 because it scatters the light andhence does not allow the light to pass through the phase shift material.The defect therefore appears as a dark spot in transmission. Slowlyvarying surfaces 172 or incorrect thickness of the phase shift layer,such as depicted in feature 161, are detected by interferometricmethods, as explained below.

A perfectly flat surface, such as 173 at the top of 162, parallel to theplane of the mask and with an optical path length L will produce fringesas the mask is scanned because, due to the tilted mirror 118, thewavefront of the reference beam is not parallel to the plane of thesubstrate. (In order to avoid any ambiguity in the direction of thechange of the phase, the tilt of mirror 118 should be greater than themaximum expected slope of any surface such as 161.) The detector outputin such a case is a sine wave, such as that shown in FIG. 11. A similarflat surface located at a path length L+d (see FIG. 10) will produce asine wave of the same frequency but with a phase shift D with respect tocurve 200. This second sine wave is shown as wave form 202.

As the mask is scanned in the Y-direction, the transmitted lightdetector 34 detects whether a particular pixel is fully transparent.Only at such fully transparent pixels are reflected light intensitymeasurements taken and digitized. At such pixels, the reflected lightintensity is determined and digitized. This is suggested by thedepiction at the bottom of FIG. 10, where during the time that the scanis passing across the non-transparent feature 164, as determined by theoutput of detector 34, the output of detector 36 is ignored. From theintensity value, and from the Y-coordinate of the pixel, together withthe values of A, w and I_(o) determined during the calibration,electronic subsystem 20 determines D in Equation 2 and the correspondingpath length variation at the pixel, i.e., the height d of the featuresurface above plane 174.

Due to the periodic nature of a sinewave, there is an ambiguity becausepath length variations corresponding to a phase shift of 360 degrees areindistinguishable. However, sudden variations resulting in a 360° phaseshift can occur only when the phase shift material contains a ridge.Such a ridge produces diffraction which is then detected in thetransmission mode. Hence, the ambiguity due to a 360° phase shift isresolvable and it is possible to continuously, at every pixel, track thethickness of the phase shift material.

In practice, the mask substrates are not likely to be perfectly parallelto the image plane, nor is the substrate likely to be perfectly flat.However, these variations are gradual, and on a 5× phase shift mask oneneed consider variations only within a radius of 4-5 microns.Specifically, only the relative phase shift between two adjacentfeatures is important, such as the relative phase shift betweenlocations 180, 162 and 184. These points are likely to be less than 4microns apart.

To determine whether there is a phase error of sufficient magnitude toindicate a defect on the substrate, the path length is computed at eachtransparent pixel covered by phase shift material 162 (FIG. 10). Thisvalue is then compared with the average of the path lengths of twoadjacent points where there is no phase shift material, such as points180 and 184. If the difference in path length differs from an acceptablevalue by more than a predetermined threshold value at the print wavelength, such as 10 degrees for example, the phase shift materialthickness at the inspected point is marked as defective.

In addition to making path length comparisons between points ongeometric features in the same vicinity, the system also checks for amissing or extra geometric feature, such as may occur in the patterngeneration. In die-to-die mode, the path lengths of pixels at 173, 180and 184 (FIG. 10) of the die 33 (FIG. 3) are compared with the pathlengths at the corresponding pixels of die 35. This comparison willdisclose any missing geometric features, unless both dice 33 and 35 havethe same error. Similarly, in die-to-database mode a comparison can bemade between the path lengths associated with the previously designatedpixels and the description of these pixels in the CADs database.

Alternate Phase Shift Measurement Method

The above measurement technique uses a digital approach to determine therelative optical path length at grid points to determine the phase shiftangle at every point. As explained below, one may also employ an analogmethod to find the phase shift angle.

FIG. 12 illustrates the additional circuitry required by this method forinsertion into the apparatus of FIG. 1 at 208 to determine the phaseshift angle. The analog signal derived from detector 36 is fed to oneinput 209 of an analog phase detector 210 which also obtains anothersignal at 211 from a numerically controlled oscillator 212. A signalproportional to the phase difference between these two signals isconverted to a digital form by an eight bit A/D converter 214 and passedto an encoder 216 and also to a digital low pass filter 218. The digitalfilter 218 and the encoder 216 are gated by a gating signal derived fromdetector 34. The digital filter 218, which functions as an integrator,accepts an input only when detector 34 indicates that the mask istransparent at the inspected point. Encoder 216 accepts the 8-bit outputsignal of the A/D converter 214 and shifts it right one bit. If thepixel is transparent at that point, the encoder inserts a 0 into themost significant position of the digital signal and transmits theremaining signal to subsystem 20 as the phase signal. Should detector 34indicate that the pixel is opaque, the digital signal will be encoded asall ones, 11111111. This signifies to the subsystem 20 that the phasesignal is invalid and should be disregarded.

The previously explained circuitry is a phase-locked-loop that followsslow variations of the phase, as might be caused by slowly varyingphenomena, such as imperfect flatness of the mask. The output of theencoder 216, when valid, indicates the path length variation in thelocal area.

Alternate Phase Shift Optical System Implementation

In some instances it is desirable to measure the actual phase shift,rather than infer the phase shift from the relative path length. Thismay be done by using transmitted interferometry. FIGS. 13 a and 13 b aresimplified schematic diagrams, in which for simplicity many of theelements shown in FIG. 2 are omitted, but illustrate a variation of thepreferred embodiment that permits measurement in either or both atransmit mode or a reflected mode using respectively transmitted lightinterferometry and simultaneous measurement of the reflected andtransmitted interference pattern.

As depicted in FIG. 13 a, to implement this alternative operating in thetransmit mode, a pellicle beam splitter 230 is added which reflectslight received from splitter 60 and produces a reference beam atdetector 34 via the path 231 past tilted mirror 232, objective lens 234and another beam splitter 236. The interference of the reference beamand the imaging beam passing along path 240 and through substrate 14 isdetected at detector 34.

In the reflected light mode, reference light split by splitter 60 isdirected along the path 250 to tilted mirror 118 and returned todetector 36 where it interferes with imaging light passing throughsplitter 60 and along the path 260 to substrate 20 where it is reflectedback along path 260 and reflected by splitter 60 into detector 36.

This alternative also permits the simultaneous measurement of the phasein both the reflected and transmitted modes.

Because lasers have a limited coherence length in both the reflected andtransmitted interference modes, the path length should be approximatelythe same for the imaging beam path and the reference beam path.

Line Width Measurement

FIG. 14 shows a plan view of a small portion 270 of a mask. Area 272 istransparent and is crossed by a feature 274 that may either be opaque(chrome or other material) or transparent if the quartz substrate of themask is covered by phase shift material. The system measures theintensity at equidistant grid points, depicted at 276. As explained morefully below, these intensity measurements are then used to determine theline width, i.e., the distance 278 across feature 274.

Each of the grid points 276 the intensity is the convolution of thepoint spread function of the optical system with the transmissivityprofile of the feature. Typically, the transmissivity profile is a stepfunction. Therefore, for a straight feature, as is shown in FIG. 14, theintensity measured at a particular grid point is a function of theperpendicular distance from the grid point to the edge of the feature(line 274). The intensity at a particular point in the vicinity of afeature can thus be interpreted as the perpendicular distance from thepoint to the line. This interpretation is done in a simple table look-upoperation in the computer 24 (FIG. 1). On the basis of the intensitiesat grid points 280 and 282, distances S₁ and S₂ are known and the slopeof the edge relative to a feature is:${\tan\quad G} = \frac{S_{2} - S_{1}}{\left( {a^{2} - \left( {S_{2} - S_{1}} \right)^{2}} \right)^{1/2}}$where a is the distance between the grid points 280 and 282 and G isangle 284.

Once the slope of the edge of a feature (line) has been determined, theopposite edge of the line can be similarly located, and a verificationcan be made that it is parallel to the previously calculated line edge.On the basis of the intensities along the two edges of the line, thelinewidth is calculated in control computer 24.

The previously described method of line measurement is, strictlyspeaking, normally applicable only to conventional masks which have nosurface areas covered by phase shift material. However, the techniquedescribed above may also be used for the measurement of phase shiftfeatures because, at the boundary between a clear area and an areacovered by phase shift material, diffraction of the incident light beamwill occur and along this narrow boundary no light will be transmitted.The line width is the distance between the center of one boundary andthe center of the opposite boundary.

It is anticipated that various alterations and modifications thereofwill be apparent to those skilled in the art. For example, to avoid theneed to sweep the laser beam during the scanning operation, instead ofusing the linear detector 34 in the preferred embodiment, one could usea time delay integrating sensor of the type described in theabove-referenced Levy U.S. Pat. No. 4,579,455. With such modification,if a laser is used as the light source, coherence in the Y-directionwould have to be destroyed by using a rotating ground glass. Thecoherence in the X-direction is destroyed by the time delay integratingsensor.

The system further includes an inspection system and method thatrepresents a major departure from the traditional die-to-die comparisonmethod of substrate inspection. With the well known and widely useddie-to-die (or die-to-data base) comparison technique, thecharacteristics of the substrate under inspection are compared toanother like substrate or a data base that is known to be correct. Thatrequires the simultaneous processing of the same information with twooptical columns for the die-to-die for both the die under inspection andthe sample to which it is being compared which is both hardware andcomputer processing intensive.

As will be seen in the discussion that follows, the second aspect of thesystem performs all of the inspection tasks using only a single opticalcolumn and only the substrate to be inspected. This is accomplished byanalyzing the relationship between two or more of the transmitted andreflected light signals from that substrate and derived functions ofthose signals, the relationship between those light signals, and therelationship between each of the transmitted and reflected light signalsand the second derivatives of those light signals.

APSM/EPSM System Overview

The basic structure of the APSM/EPSM system is shown in the simplifiedview of FIG. 15 and the more complete view of FIG. 16. This system isvery similar to the simplified and detailed FIGS. 1 and 2. Thedifference between FIG. 1 and FIG. 15 is that data base adaptor 18 isnot required needed for the current aspect of the system. Similarly, thedifference between FIGS. 2 and 16 is that the phase shift/line widthmeasuring components that extend to the left from beamsplitter 60 arenot necessary to perform the function of the current aspect of thesystem. The phase shift/line width measurements could be performed usingthe same transmission data as that used for inspection by the techniqueof the current aspect of the system. From FIGS. 15 and 16 it can be seenthat the automatic optical inspection system 10 includes threespecialized subsystems: a laser/optical subsystem 11; an x-y stage andservo drives 12 subsystem; and an electronics control and displaysubsystem 19. FIG. 15 also shows a substrate 14 on X-Y stage 12 that isto be inspected for defects.

Transmitted and reflected light inspections can be performed eithersimultaneously or mutually exclusively in time with the requirements forthe illumination light beam on, and the position of, substrate 14 beingas discussed above.

Briefly, the underlying theory of operation, which is discussed morecompletely below, is the ability to compare signals corresponding to atleast two of the detected transmitted and reflected light beams andfunctions of each of them being able to disclose the existence of adefect. The two measured values of the system are the intensity of thelight beam transmitted through the substrate as sensed by transmissiondetector 34, and the intensity of the reflected light beam as detectedby reflected light detector 36. Those two measured values can then beprocessed to disclose the type of defect, if any, at a correspondingpoint on the substrate. As an example, either a chrome dot or a particleon a substrate is opaque and hence will result in a “dark output” (lowsignal output) from transmission detector 34, with the reflective chromedot defect on the substrate producing a high reflected light indicationwhile the particle will typically reflect less. Thus, the use of bothreflective and transmissive detection, for example, one may locate aparticle on top of chrome geometry which could not be located if onlythe reflective or transmissive characteristic of the defect wasexamined. In general, one may determine signatures for certain types ofdefects, such as the ratio of their reflected and transmitted lightintensities. This information can then be used to automatically classifydefects.

X-Y Stage and Servo Drives 12

X-Y stage 12 is a precision substrate driver under control of electronicsubsystem 20 and capable of moving substrate 14 under test in aserpentine fashion, within a single plane, relative to the optical axesof optical subsystem 11 so that all, or any selected part of, thesubstrate surface may be illuminated and therefore inspected.

In a typical inspection system of the second aspect of the presentinvention, stage 12 is an air-bearing X-Y stage that is driven by alinear motor, or servo, on each axis with the position of stage 12 alongeach axis monitored by interferometers (not shown), such as a model TIPSV, made by Telectrac Corporation.

Electronics and Control Subsystem 19

The electronics and control subsystem 19 includes several elements shownin FIG. 1. Included are electronic subsystem 20, control computer 24,keyboard 26 and display 22. Keyboard 26, in communication with controlcomputer 24, and display 22, in communication with electronic subsystem20, provide the user interface to the inspection system of the secondaspect of the present invention. Additionally, electronic subsystem 20is in communication with x-y stage 12, transmission and reflected lightdetectors 34 and 36, and control computer 24.

Control computer 24 acts as the operator console and master controllerof the system and is a device such as a SPARC computer made by SunMicrosystems of Mountain View, Calif. with all system interfaces withthe operator and the user's facilities made through control computer 24.

Commands are issued to and status is monitored from all other subsystemsand components so as to facilitate completion of the operator assignedtasks.

The function of electronics subsystem 20 is to interpret and execute thecommands issued by control computer 24. These functions are: digitizethe input from transmission and reflected light detectors 34 and 36;compensate these readings for variations in the incident lightintensity; accumulate the output of the interferometers used to trackthe stage 12; provide the drive for the servos of stage 12; and monitorsensors which indicate status.

Operational Theory

Transmission detector 34, instantaneously and continuously, generates atransmitted light signal 15 in proportion to the light transmittedthrough substrate 14 and received by transmission detector 34.Transmitted light signal 15 is then amplified and offset in electronicsubsystem 20 to normalize the peak-to-peak signal amplitude to values of0 to 1. Similarly reflected light detector 36, instantaneously andcontinuously, generates a reflected light signal 17 in proportion to thelight reflected from substrate 14 and received by reflected lightdetector 36. Reflected light signal 17 is similarly normalized inelectronic subsystem 20.

For purposes of discussion, substrate 14 is assumed to have an opaquelayer that covers a portion of the underlying material of substrate 14.That opaque layer will reflect a greater portion of incident laser light13 than is similarly reflected from the surface of the bare underlyingmaterial of the substrate. For example, it is known in the art that at awavelength of 488 nm, anti-reflective chrome (opaque layer) has areflectance of 11% and quartz underlying material of a substrate has areflectance of 4.5%.

FIG. 17 illustrates a hypothetical model for the normalized transmittedand reflected light signals 350 and 352, respectively, for a scan acrossa substrate with the abscissa of FIG. 17 being time, or distance acrosssubstrate 14, as light beam 13 is advanced relative to the surface ofsubstrate 14. When light beam 13 scans a bare section of substrate 14having a quartz underlying material, the normalized transmitted lightsignal 350 is at level 1 and the normalized reflected light signal 352is at level 0, as shown in region 340 of FIG. 17. he ordinate associatedwith FIG. 17 therefore represents the grey scale of the transmission orreflection. When light beam 13 scans a region of substrate 14 having anopaque layer, the normalized transmitted light signal 350 is at level 0and the reflected light signal 352 is at level 1 as shown in region 342in FIG. 17. In the case where light beam 13 is at an edge of an opaquelayer, or feature, on substrate 14, the normalized transmitted lightsignal 350 transitions from level 1 to level 0 while the normalizedreflected light signal transitions from level 0 to level 1 asillustrated in region 341 of FIG. 17.

This hypothetical model assumes that the transmitted and reflectedsignals at the same point on substrate 14 are always complementary toeach other in the absence of defects, so that their sum 354 is alsoinvariant in the absence of defects. This behavior is represented inFIG. 17 by summation signal 354, which is offset by 0.5 from each ofsignals 350 and 352. Thus, such behavior would allow any observeddeviation in the summation signal to be interpreted as a defectdetection.

Referring now to FIG. 18 the typical signals observed for a realizableoptical subsystem are illustrated in a manner similar to that of FIG.17.

Included are transmitted light signal 370, reflected light signal theleft most region that corresponds to region 340 of FIG. 17. In thatregion of FIG. 18 the signal values are typical for inspection over theclear substrate with no opaque over-layer or defects present. In thecenter region of FIG. 18, a blip 373 in the normalized reflected lightsignal 372 and a resultant blip 376 in the summation signal 374 bothresult from a particulate contamination on top of an opaque layer onsubstrate 14. In the right most region of FIG. 18, blip 371 in thenormalized transmitted light signal 370, the corresponding blip 375 inthe normalized reflected light signal 372, and resultant blip 377 in thesummation signal 374 all are caused by a particulate contamination on atransparent substrate 14.

In such a situation as shown in FIG. 17, summation signal 374 alsodeviates from the constant 0.5 level in the transition regions (similarto regions 341 in FIG. 17) of the plot. These transition regionscorrespond to those portions of substrate 14 that are near the edges offeatures thereon (e.g. boundaries between opaque layers on the substrateand bare underlying substrate regions). Such a deviation appears in FIG.18 as blip 378. Deviations such as blip 378 are due to variations in thelight scattering behavior at edges of features on substrate 14, andmismatches between the partial coherence parameters of the transmittedand reflected optical paths (see FIGS. 15 and 16). Typically thesedeviations in the summation signal at feature edges can be of roughlythe same size as a blip 377 caused by submicron contamination onsubstrate 14. Therefore, detection of defects by the summation of thereflected and transmitted light signals 17 and 15, respectively, doesnot provide an adequate method for distinguishing submicron particulatecontamination from feature edges on substrate 14.

One extension of the method to enable a realizable optical subsystem toautomatically distinguish between surface features and defects of asubstrate 14 is discussed below in relation to FIGS. 19-22.

FIG. 19 illustrates the relationship between a family of pairs ofnormalized transmitted and reflected signal values with each pair ofvalues occurring at a particular point on the surface of substrate 14 aslight beam 13 is deflected over substrate 14, showing the correlationbetween each of the two signal pairs with no defects present at anypoint on substrate 14. In FIG. 19 the normalized transmitted lightsignal is plotted on abscissa 400 and the normalized reflected lightsignal from the same point on substrate 14, is plotted on ordinate 401for each pair of signals from each inspected point on substrate 14.

As discussed above in relation to FIG. 15, electronic subsystem 20normalizes and offsets the transmission and reflected signals 15 and 17to range between 0 and 1. Thus, for example, region 450 of FIG. 19represents signal pairs from a substrate region where there is a muchgreater reflected signal than transmitted signal which could representan opaque layer on the substrate at that point. This results since anopaque layer attenuates the light beam resulting in a small lighttransmission value, and at the same time, that opaque layer reflectsapproximately 11% of the incident laser beam to reflected light sensor36. Similarly, region 452 of FIG. 19 can be seen to represent thecondition where laser beam 13 scans a bare region of a quartz substrate.Values in region 452 result from a point on substrate 14 that transmitsa large portion of light beam 13 resulting in a high detectedtransmitted light value, while t the same point on the substrate onlyabout 4.5% of the incident laser beam 13 is reflected resulting in asmall detected reflectance value. Thus, the intermediate region 455 inFIG. 19 represents points on the surface of substrate 14 where lightbeam 13 is scanning the edges of features.

A typical transmission-reflection relationship in T-R space (thecoordinate plane defined by T and R orthogonal axes) for a realizableoptical system is shown by curve 420 enclosed within a uniform tolerancearea defined by envelope 421. (Note that the shape of curve 420 willvary depending on several factors: the operational characteristics ofoptical subsystem 11; as well as the materials of the underlying layerand surface layers of the features of substrate 14. Each combination ofoptical subsystem and substrate design therefore will have its owncharacteristic curve 420 in the T-R space.)

Thus, each inspectable point, or pixel, on substrate 14 can berepresented in the T-R space by a point with coordinates correspondingto the transmitted and reflected signal values produced at that pixel.Those pixels with transmitted and reflected signal values which fallwithin tolerance envelope 421 are considered to be defect free while allothers represent either defects or system noise. The tolerance to whichthe inspection is to be performed, and hence which transmitted andreflected pixel pairs will be considered defective, is determined by thewidth of envelope 421 and the distance of its boundary from curve 420.The width of envelope 421, and hence the inspection tolerance, can bevaried parametrically by position along curve 420 so that a user mayestablish a tighter tolerance against more harmful types of defects anda more relaxed tolerance against other types of defects. For example,the defect identification sensitivity over bare areas of the substratecan be independent from the identification sensitivity of defects overthe opaque areas of the substrate. One could even have a complex set oftolerances that span the entire T-R space (i.e. the width of envelope421 does not have to be uniform along T-R curve 420).

Thus, one feature of the second aspect of the present invention is a T-Rspace coordinate plane as in FIG. 19. Thus, if the T-R point for anypoint from substrate 14 falls outside the selected tolerance envelopedefined by boundary 421, a defect is identified whether or not theactual coordinates of the point of the defect are known. Keep in mindthat there has as yet been no discussion in this section of thespecification of the alignment of the substrate and the maintenance ofany coordinates of defects in memory. Since the present inspectionsystem is not a comparison system, as in the prior art, it is notnecessary to know the physical location of a defect on the substrate todetermine that there is a defect. All one needs to do is to select thetolerance that is acceptable for each type of surface characteristic andif a T-R measurement falls outside of envelope 421 the substrate isdefective. It is also not important to the method of the current aspectof the system that the points in the T-R space be contiguous for themethod to find defects. For example, the first point may fall in region450, the next 55 points within region 425, then 6 points in region 450again, one point in region 455 and then 2 points in region 452, etc. Thesequence is unimportant to the ability of the second aspect of thepresent invention to identify the presence of a defect.

Also, as discovered during the development of the second aspect of thepresent invention, the location of the T-R point in the T-R plane alsoconveys information about the physical properties of the pixel elementon the surface of the substrate and, in the case of a defect, the typeof defect found. Thus another feature of the second aspect of thepresent invention is the use of the T-R detection space for automaticdefect classification.

Given that discovery, the defect detection process of the second aspectof the present invention includes at least the ability to identifydefect types using T-R space. To do so, the non-defective region in T-Rspace defined by boundary 421 must be determined so that any T-R pairfor an inspected pixel on substrate 14 can be instantly determined as adefect or a non-defect point by whether it falls inside or outside thenon-defective region within boundary 421. Furthermore, the location ofthe T-R point could, if desired, be analyzed to identify the type ofdefect that was detected.

Methods for bounding the non-defective region and the various defectclassification zones, collectively referred to as the T-R reference map,are discussed further below. Additionally, since the defect detectionprocess depends only on the two measured signals T and R at a singlepoint of the substrate, and does not depend upon the comparison of testand reference images (die-to-die or die-to-data base) as taught in U.S.Pat. No. 4,926,489, no alignment of the substrate with the defectdetermination system of the second aspect of the present invention isrequired.

It has been observed that the use of a global alignment step of thesubstrate to a reference grid would further enable the system todetermine the location of the defect on substrate 14 as well, if theuser should so desire. However, as stated above, for a pass/fail defectdetermination test the physical location on the substrate of the defectis unnecessary.

FIG. 20 is a plot of a typical T-R reference map that illustratesvarious defect regions which one might encounter with the type ofsubstrates currently of interest. For example: particulate contaminationon anti-reflective chrome would have low T values and intermediate Rvalues as represented by region 470; particulate contamination on anotherwise bare quartz region would have low R values and intermediate tohigh T values as represented by region 474; particulate contamination onthe edge of a feature could have a broad range of T and R values fromboth being low to one being high while the other remains low asrepresented by region 472; a missing anti-reflective chromium layerwould have a high R value and a low T value as represented by region478; very large defects would have very low values of T and R asrepresented by region 480; and the presence of thin residual chrometransmission defects would have T and R values that are to the right andabove characteristic curve 420 as represented by region 481.

For some types of defects, analysis of the T-R point may not be asensitive enough indication of the presence of a defect (i.e. thevariation of either the T or R value may not be sufficiently indicativeof a problem given the corresponding other value for a particular pixelon substrate 14). An example of this type of defect is an inclusion in aquartz substrate, wholly contained below the surface of a mask. In thattype of defect the change of the transmission value, T, occurs withlittle or no corresponding change in the reflectance value, R. As can beseen from FIG. 20, nominal reference curve 420 has a small slope forlarge T values. Therefore a change in the transmission value alone inthis region may not result in a T-R point outside envelope 421 and hencewill be difficult to detect, or go undetected, in T-R space alone.

However, if the second derivative of the normalized transmission value,T″, which identifies the presence of edges in the image, is plottedagainst the normalized transmission signal, T, as in FIG. 21, thosetypes of defects can be identified. The nominal behavior for pixels fromdefect-free points on the substrate correspond to the curve 503,enclosed by tolerance envelope 506 as shown in FIG. 21. The coordinateplane such as in FIG. 21 is referred to as the T-T″ detection space, andis another feature of the second aspect of the present invention.

As with T-R space, T-T″ reference map is derived by identification ofthe defect-free region, here within envelope 506, and other specificregions of interest in T-T″ space. In this case, a change intransmission, resulting in a T-T″ point within locus 520, outside thenon-defective region in envelope 506, tentatively indicates atransmissivity defect, although such a point could occur when the pixelis located near the edge of a feature and not be a defect at all. Thus,T-T″ space alone could not be relied on to make the necessarydistinction in such a situation.

During the development of the system, it was also discovered that it maybe useful to examine the reflected signal, R, plotted against the secondderivative of the reflected signal, R″. FIG. 22 similarly illustratesthe R-R″ space which is also a feature of the second aspect of thepresent invention. Curve 603 represents the nominal relationship betweenR and R″, and region 607 represents the region of tolerance fornon-defective pixels with the R-R″ detection space also divided intodistinct regions of interest. One of those regions of interest includesthe tentative defect-free region within curve 607, to form an R-R″reference map. Other regions of interest include locus 605 with thepoints therein potentially resulting from the laser scanning an opaquelayer where the R value is high, the T value low, and the value of R″ isalso low. Another region of interest is within locus 609 where pointstherein tentatively result when the laser scanning beam illuminates apoint on a bare substrate. The third region of interest is the locus ofpoints 630 which is typical of an illuminated pixel that is notpositioned on the edge of a feature when there is a correspondingreading of that pixel in region 520 of the T-T″ space (FIG. 21). Withboth of those conditions being met, the pixel of interest corresponds toa transmissivity defect on the sample. Finally, where there is a residueon an opaque layer, associated pixels in R-R″ space will be in region620, with the corresponding values of R and R″ signifying the presenceof a reflectivity defect.

Thus, to positively identify and classify as many defects of substratematerials at a point on a substrate, it is necessary to determine whichdefect regions are occupied by the coordinates of the point in each ofthe T-R, T-T″, and R-R″ spaces. With that information, electronicsubsystem 20 can reduce coordinate information into region informationand generate an independent defect region report for each space with acode to indicate the region (e.g. 452, 455, 470, 472, 474, 478, 480,481, 505, 507, 509, 520, 605, 607, 620, 630, etc.) occupied by thecoordinate in that space. Further, with such a region report availablefrom each detection space, electronic subsystem 20 can then logicallymerge the region reports from each of the detection spaces to synthesizea final defect report that comprehensively encodes the results collectedfor that point on the substrate and reports whether a defect wasindicated by those results. That final defect report would thus indicatea pixel type code and a binary defect indicator value that indicateswhether or not a defect is present. With this information at hand, thesystem can also be programmed to produce other types of reports,including one that totals the number of points having each type ofdefect over the entire substrate.

Many defect types can be found simply by the occurrence of a defectindication falling within the coordinates of a defect detection regionin only one of the two function spaces (i.e. T-R, T-T″ or R-R″ space).However, as explained above, for example, certain transmissivity defectscan only be detected by an occurrence of a defect indication in both theT-T″ space in region 520 (FIG. 21) that corresponds to a defectindication in R-R″ space in region 630 (FIG. 22). In those instances, afinal transmissivity defect report is generated only if those defectoccurrences are indicated by both region reports in the two differentspaces. That type of report is generated by a logical AND operation thatdetermines if the T-T″ space reports a coordinate in region 520 AND theR-R″ space also reports a coordinate in region 630. Positiveverification of both occurrences then produces a final report thatindicates the presence of that type of transmissivity defect.

Thus, the final defect detection and classification procedure is carriedout by merging the various region reports. Some types of defects may beconditional on multiple reports, such as the transmissivity defectdiscussed above, while other types of defects may be unconditionallyindicated by single individual reports. From a hyperspace perspective,some defects can be determined in two dimensional space (e.g. T-R, orT-T″ or R-R″ space individually, that is from a single report asdescribed above), while others can only be determined in threedimensional space (e.g. T-R-T″). Others may require four dimensionalspace, or five dimensional space, etc.

Electronic subsystem 20 is thus programmed to perform the necessarycombinations of logical operations in the required order to generate afinal defect report that identifies the defects of interest from thecollected region defect reports. The final defect report is generated byfirst combining all the conditional reports by AND operations in theproper sequence, and then ORing those results with the other reportswith the final defect report thus indicating a defect if any of theindividual region reports indicate a defect, by either conditional orunconditional detection (i.e. from two, three, four, five, . . . spacein the hyperspace model), and provides a defect type code that indicateswhich regions from the defect region reports were responsible fordetermining the presence of the defect.

In practice, given the materials of current interest, the various defecttypes are detected by fusion of the analytic results obtained from thethree individual detection spaces, which involve the four pixelobservables T, R, T″, and R″, which have been discussed above as beingplotted pairwise in three individual two-dimensional coordinate planesto simplify the initial consideration of the method of the second aspectof the present invention. In reality, the defect detection process ofthe second aspect of the present invention occurs within afour-dimensional observation hyperspace with coordinate axes T, R, T″,and R″, with the four observables from each pixel forming afour-component vector. Additionally, this hyperspace can be subdividedinto various hyperdimensional classification regions as illustrated inFIGS. 20-22, and even T-R″, R-T″, and T″-R″ spaces, if for somematerial/defect combination those spaces would also be of interest inthe resolution of whether a particular type of defect is present thatcan not be determined from one or more of the previously discussedspaces. Since the nominal defect-free behavior of an inspected substratewill contain a high degree of correlation in this four dimensionalhyperspace, the second aspect of the present invention takes advantageof this redundancy by analyzing the observables in pairs, projecting theobservable four-vectors onto selected two-dimensional coordinatesubplanes, essentially decomposing the four-dimensional observationspace into three individual two-dimensional subspaces for simplervisualization, calculation and identification of defect types.

Thus, it is easy to visualize that alternate embodiments of the secondaspect of the present invention might effectively utilize other possiblecombinations of the four observables for detection analysis, T versus R″for example. Furthermore, such alternate subspaces need not be limitedto two-dimensional projections of the observables, and in principle mayextend to utilization of the entire four-dimensional representation fordetection analysis as inferentially discussed above.

Furthermore, other observables might be generated by performingalternative filtering operations on the measured T and R signals asrepresented by the block diagram of FIG. 24. For example, the measured Tand R signals and additional high-order signals derived from themeasured signals to create image maps. Thus, in addition to the secondderivative functions as discussed above, larger convolution operatorswith unique coefficient values might also be used to produce othersignals to analyze to more clearly reveal other characteristics of asubstrate of interest. In general, given an arbitrary number ofobservables (e.g. derivatives of various levels, signal rangelimitations of which selected derivatives may be taken, integrations, orany other type of function that can be generated from the measured T andR signal values), they may be analyzed within an arbitrary number ofsubspaces of the observation space, of arbitrary dimensionality lessthan or equal to the number of observables (two to n dimensional space).As already discussed above, comprehensive defect detection usinglower-dimensional subspaces of the observation space generally requiresthat all the information from all reporting subspaces be collected andmerged for final evaluation.

FIG. 24, more specifically, illustrates a general case for performing Moperations on the actual T signal 700 and N operations on the actualreflected signal 702 from the transmission and reflected pixel image mapof the surface being inspected. Each of those various operations in thefirst tier of blocks are identified by a series of filters identified asf_(x)(T) or g_(y)(R). For the process described above, filters f₁(T) 704and g₁(R) 706 are each all pass filters, and filters f₂(T) 708 and g₂(R)710 are each second derivative filters to form the signals T″ and R″,respectively, from the input T and R signals.

The other filters that might be included in the first tier of operationsare illustrated here as f_(M)(T) 712 and g_(N)(R) 714 which may performanother function on the input T and R signals to form other signals thatwould be useful to identify another feature of interest on substrate 14.

The second tier of operations in FIG. 24 is the combination of thevarious signals from the first tier filters in two dimensional space ofeach possible combination, or at least those of interest, of the signalsfrom the first tier filters. For example, if f₁(T) 704 and g₁(R) 706 areeach all pass filters, and filters f₂(T) 708 and g₂(R) 710 are eachsecond derivative filters, then in block 716 the T-R space informationwould be collected, in block 718 the T-T″ space information iscollected, in block 720 the R-R″ space information is collected, inblock 722 the T-R″ space information is collected, in block 724 the R-T″space information is collected, and in block 726 the T″-R″ informationis collected. The other blocks in this level illustrate the collectionof other combinations of signals to present the values of those signalsin the corresponding two dimensional space. The results of each of theblocks in the second tier are provided to the third tier which consistsof a logical defect merge function 728. The logical defect mergefunction 728 could be implemented with a microprocessor programmed toidentify values of the signal pairs in each of the blocks of the secondtier that represent a particular defect, and then to prepare acomprehensive defect report of all of the defects that were revealed byeach of the signal pairs of the various blocks in the second tier.

Computation of the Second Derivative

Although this transformation is not a point operation function, a localneighborhood of pixels adjacent to the pixel in question is the onlyrequisite of image integrity for computation and analysis of the secondderivative. All features of this aspect of the system involve thereduction of reference data to a coordinate-free statisticalrepresentation. The reference map does not contain information about theexpected substrate behavior at any specific location, but ratherrepresents the statistical behavior at some point on the substrate withthe entire substrate, or region of interest of the substrate, beinginspected.

This aspect of the system has no requirement for direct comparisonbetween test images and reference images, whether from an adjacentidentical lithographic pattern or CAD database, and hence an alignmentsystem is still not required.

Prior to discussing the second derivative method in particular,attention is directed to FIG. 23 a where a pixelized transmission valueimage of the region of interest of a substrate 14 is illustrated. Forpurposes of discussion the image is shown as a matrix of individualpixel transmission values, t_(x,y), for an image that is n×m pixels insize. A pixelized reflection value image would be similar and of thesame size for the same region of interest of substrate 14. Such areflection value image can be visualized by replacing the variable “t”in FIG. 23 a with an “r”.

The purpose of the second derivative computation is to provideinformation about the proximity to an edge of a feature or defect. Thesecond derivative computation is a linear convolution operation on thegiven image. At every pixel in the image (e.g. t_(x,y) in FIG. 23 a), alocal rectangular neighborhood of pixels, with the current pixel at thecenter (e.g. for a 3×3 operation): t_(x−1,y−1) t_(x−1,y) t_(x−1,y+1)t_(x,y−1) t_(x,y) t_(x,y+1) t_(x+1,y−1) t_(x+1,y) t_(x+1,y+1)is input to a linear operation by a rectangular matrix, L, of the samesize (3×3 in this example) to produce a single output value for thesecond derivative value of the central pixel (t_(x,y)=>t″_(x,y)) at thatpoint.

That convolution can be represented as follows:[T″]=[L]∘[T]

Thus, the value of each element of the second derivative image asdefined in FIG. 23 b can be expressed mathematically as:${T_{s}^{\prime}\left( {x,y} \right)} = {\sum\limits_{ij}^{- {ltol}}{{L({ij})}{T\left( {{x - i},{y - j}} \right)}}}$

In this operation, however, there is an erosion at the edges of thetransmission pixel image in that the resulting T″ image matrix as shownin FIG. 23 b has no values in the outer most rows and columns around theedge of that image.

The selection of L for performing the second derivative function cantake many forms. The values for L illustrated here is a Lambertiantechnique that is common in the art, and one that is symmetric in twodimensions since the transform here is being performed in twodimensions. L in this example has been selected to have a spectralresponse in the other domain that is as circularly symmetric aspossible.

Thus, through this operation, the T=>T″ and R=>R″ transformations areperformed. For purposes of illustration here, the second derivative ofthe T or R pixelized images is computed by approximating it with a highpass filter, L, or convolution matrix: $L = \begin{matrix}d & v & d \\h & c & h \\d & v & d\end{matrix}$where typical values are: c=0.1817536, d=0.01539003 and v=h=−0.0648287.

An alternate implementation to the convolution operation on digitizedpixel data is to optically process the image using the well-knownFourier filtering techniques with coherent light to perform thehigh-pass filtering before the sampling process.

Methods of Reference Map Generation

As mentioned above, the T-R, T-T″, and R-R″ detection spaces areutilized to characterize the behavior of a substrate under inspection bybounding the non-defective regions in each detection space. In fact, thesuccess of the current method rests on the ability to define theboundaries of the defect-free regions in the T-R, T-T″ and RR″ spaces.The definitions of those regions for each reference map are necessaryfor satisfactory defect detection; additionally, each reference map maycontain defect classification zones which are adjustable as desiredaccording to the response of the defects to the inspection process.

Experiments have shown that substrate characteristics vary sufficientlyso that the defect-free boundaries have to be determined for eachsubstrate in order to optimize the inspection sensitivity for thatsubstrate. On the contrary, the defect classification regions are moregeneric to the inspection method than the substrate, and errors inclassification have a lower cost value than errors in detection.Therefore, the classification zones will be adjusted less frequently inpractice, and can be adjusted by heuristic and long-term statisticalanalysis of defect characteristics.

The purpose of this section is to explain how nominal non-defectivesubstrate information is obtained and encoded in the detection space(i.e. experimental determination of shapes and tolerance regions ofdefect free zones in T-R, T-T″ and R-R″ spaces). Analytical methods fordetermining the defect-free region in the T-R reference map aredescribed below with these methods carrying over to the determination ofthe defect-free boundaries for the T-T″ and R-R″ reference maps.

Generally, reference mapping can be regarded as a training process wherethe nominal behavior of the substrate type is observed by arepresentative sampling of a defect-free area of a selected number ofsubstrates of the same type to account for production tolerances. Itshould be noted that reference curves can be developed using a singlesubstrate, however, the use of several provides a better statisticalaverage and the possibility of overcoming the inclusion of a defect onone of the sample substrates.

Thus, each substrate used for set-up of the system is sampled in thesame area and the T, R, T″, and R″ signals measured at the samplepoints, and those signals are then plotted in detection space as arecord of the sample population.

Filtering (described below) is then performed on this data record inorder to approximate the true statistical behavior of defect-free pixelsfor the particular type of substrate used to develop the defect-freeareas in each of the reference maps. Thus, this process creates areference map with each point designated as defective or non-defectiveby a binary value. The reference map so developed may then be extendedto other values and further encoded for defect classification purposes.

In particular, an area on the substrate with representativephotolithographic patterns is chosen to serve as a typical referencesample for the substrates under inspection. This sample region can bechosen by an operator of the inspection system or automatically undercontrol of the system computer. With either method of choosing thereference sample region for reference locus characterization, it isnecessary to ensure, as much as possible, that the reference sample isfree of defects. Once the reference samples are chosen, transmitted andreflected light images of the reference samples in the selected regionare acquired.

At this point, a number of methods may be applied to obtain thedefect-free region in each of the reference maps. Three of these methodsare described below, each of which may be performed by automaticcomputation.

For example, a binary scatter plot of all pixels taken of the sample(s)may be generated in the T-R plane with every point in T-R space occupiedby at least one sample pixel assigned a value of one. All remainingunoccupied points in the T-R plane are assigned zero values. Typically,most of the occupied points will be concentrated within a cluster inenvelope 421 (FIG. 20) but there will also be some unoccupied pointswithin that envelope and possibly some occupied points outside thatenvelope due to anomalies in the sample.

Next this binary scatter plot is operated on to generate a contiguousarea within envelope 421 where all points have unitary value, surroundedby only zeros in the remainder of the T-R plane. To achieve this,standard binary morphological operations are performed, such asdescribed on pp. 384-389 of a book entitled Fundamentals of DigitalImage Operation, written by Anil K. Jain. (Prentice-Hall, EnglewoodCliffs, N.J., 1989.)

Typically, a dilation operation might be applied first, using asymmetric kernel that is at least large enough to remove all the gapsthat were present within the body of the sample cluster between theoriginally sized pixels. The result is a binary distribution which hasbeen filled and expanded.

Similarly, an erosion operation using a symmetric kernel can be used toproduce a reference envelope of the required size. Thus, inspectionsensitivity is controlled by adjusting the size of the final referenceenvelope, and therefore the nature and size of the final operation.

Dilation of the envelope reduces sensitivity, while erosion has theopposite effect. In general, the final envelope should be larger thanthe sample cluster since the finite sample cluster only partiallyrepresents the statistical distribution of defect-free points.

For a more accurate representation of the sample data, a multi-valuedhistogram of the reference sample in T-R space might be preferredinstead of a binary scatter plot. Using this approach an actual count ismaintained for each coordinate within T-R space as the sample substratesare scanned to develop the defect-free region in T-R space. Theresultant histogram can then be smoothed by application of anintegrating filter, and converted to a binary-valued map bythresholding.

The advantage of the histogram approach is that T-R points are weightedby their frequency of occurrence, so that rarely occurring T-R pointswill not be weighted as heavily as frequent values during integration.Also, the adjusting of the final threshold controls eliminatesanomalous, infrequently occurring, values from the final T-R referencemap. Further, the width of the integrating filter also allows somesensitivity control.

Another technique of manipulating the sample histogram to define thedefect-free locus is by multivalued morphology, as explained in a paperby Haralick et al: “Image Analysis Using Mathematical Morphology”, IEEETransactions on Pattern Analysis and Machine Intelligence, Vol PAMI-9,No. 4, July 1987. This processing approach is a multi-valued extensionof the binary morphology already cited, defining dilation and erosionoperations on functions with multi-valued range. This approachrepresents something of a hybrid of the previous two approaches, in thatmulti-valued dilations and erosions would be applied, in place of anintegrating filter, to obtain a smoothed histogram, and a finalthreshold operation would reduce the mapping to a binary value.

As discussed above, a laser scanning system may be used tosimultaneously generate transmitted and reflected light signal pairs,the method of inspection and defect classification of the second aspectof the present invention can be utilized with any image scanning processwhich is capable of generating synchronized transmitted and reflectedlight signal pairs.

Furthermore, this method of detection and classification can be utilizedwith any image scanning process which is capable of generatingsynchronized multiple light signals, generated by any number of lightdetectors placed in any direction about the substrate, which may beilluminated by any light source directed at any angle toward thesubstrate. As explained in the discussion of the detection space, thenumber and nature of the observables need not be restricted to T, R, T″,and R″, so long as there is sufficient correlation within theobservables that a reference map can be generated to performsatisfactory detection and classification.

An alternate method for detecting and classifying defects is with neuralnetwork methods. For example, the detection spaces and reference mapsdeveloped by the processes discussed-above can be implemented as aninput/output mapping with a three-layer backpropagation network (BPN) asdescribed in pp. 89-126 in a book Neural Networks—Algorithms,Applications, and Programming Techniques by J. A. Freeman and D. M.Skapura, Addison-Wesley, Reading, Mass., 1991.

For the neural network approach, requirements on the scanning system arethe same as discussed above with the following modification. A typicalBPN, illustrated in FIG. 25, is composed of three layers of neurons, aninput layer, a hidden processing layer, and an output layer. Each neuronin the input layer receives an observable (e.g. a single pixel of thelocal rectangular neighborhood of pixels discussed above with respect toFIGS. 23 a and 23 b from both the T and R signals) as an input signaland passes it to the second, or hidden, layer; each neuron in the hiddenlayer receives all outputs from all neurons in the first layer andgenerates an independent output signal; and each neuron in the thirdlayer receives all outputs from all neurons in the hidden layer andgenerates an independent output signal. Thus, each neuron creates anoutput signal based on a weighted linear activation function of thecombined inputs from all of the nodes in the previous layer. Each ofthose weighted linear activation function having been determined duringthe learning phase through variations in the individual biasing of eachnode of the hidden and output layers. The biasing functions can eitherbe calculated and then applied to the respective biasing unit, or thelearning can be performed in a dynamic environment, even an on-goingprocedure in some applications where not all possible outcomes are knownat the outset. Additional discussion of the learning phase is presentedbelow to further illustrate this process.

In operation, even though each node in the hidden and output layersreceives the output signal from each node of the previous layer, not allof those signals are necessarily used in the performance of theparticular function of that particular node. The interconnection of allnodes in the previous layer to every node in the next layer is theresult of the standardization used in the production of the BPN sincethe effect of various signals can effectively be ignored at those nodeswhere that signal is not of interest. That is, the biasing of eachhidden node can be adjusted to generate an output signal from that nodethat is an approximation of the values, for example, in the T-T″ spaceof the second aspect of the present invention while ignoring the R andR″ signals from the input layer nodes.

Thus such a BPN might have four input neurons for the four observablesT, R, T″, and R″, a hidden layer which measures many differentactivation potentials corresponding to different correlations in thedata, and an output layer which generates a set of membership values,each output neuron node assigned a membership value for a specificdefect class. The final evaluation might be determined by the class withthe maximum membership value. This implementation is actually analternative method for determining the same input-output relationship,referred to as the reference map, which is a generic aspect of thepresent invention. In fact, the input layer corresponds to thecoordinates of the detection space, the output signals correspond to thedefect class assignment for a given input signal, and the hidden layercorresponds to an optimal analytical or logical procedure for assigninga class to each input, as embodied in the reference map.

The backpropagation feature of such a network is used to train theweights in the hidden and output layers so that the desired mapping isachieved and errors are minimized. The training procedure describedpreviously can be easily adapted to this implementation by feeding thesample data through the backpropagation procedure and adjusting theweights to match desired outputs to the inputs. Furthermore,backpropagation allows the BPN to continue training during use whenevera secondary defect verification procedure is required.

Another variation to this approach applies where the network alsoperforms the filtering on the T and R signals, so that the input layerconsists of 18 input neurons, accepting the 9 transmitted values and the9 reflected values contained within a 3 by 3 neighborhood of pixels.

One particular aspect of the present invention is to extend theapplication of transmitted and reflected imaging to photomask patterninspection using die-to-die (DD) or die-to-database (DDB) comparisontechniques. Comparative pattern inspection algorithms typically locatedefects that produce significant differences between an optical image ofthe specimen and a reference image. The reference image may be from aprevious sample stored and recalled from memory (DD), or derived fromthe photomask design database (DDB).

The previous die-to-database system is illustrated in FIG. 27 a. Thesystem of FIG. 27 a receives transmitted greyscale data and performs apreprocessing transformation in block 2701. The system also performs asimilar preprocessing transformation on the reference database image inblock 2702. The preprocessing transformation converts greyscale data toimage features which facilitate the defect detection process. Thefeature transform may apply any of a variety of linear or nonlinearoperations on local or neighborhood image domains to obtain featureshaving useful attributes. The system then performs all subsequentfunctions on the optical and reference image features, including analign/interpolate/compare algorithm to determine defects, as shown inblock 2703. Block 2703 performs an alignment of the optical andreference image features, interpolates image points between recognizableoptical and reference image features, and compares the optical andreference features. Interpolation may be required due to offsets in datasampling coordinates, which can produce difference errors exceeding thedefect threshold. Based on this align, interpolate, and comparealgorithm, defects may be determined and addressed. Note that neitherthe reflected signal nor image is used in any block of thisconfiguration.

In evaluating the performance and capabilities of pattern inspectionsystems in connection with various sample or pattern types, theforegoing methods for detection of particles and contamination, usingtransmitted and reflected images, were sensitive to phase transitionsand defects found on Alternating Phase Shift Masks (APSMs). These masktypes are physically similar to common quartz and chrome photomasks,except where thickness transitions are etched in the clear material inAPSM photomasks to induce phase shifting between adjacent regions duringphotolithography. Under inspection, these phase transitions produceunique variations in the T-R, T-T″ and R-R″ detection spaces definedabove. However, phase defects, which are unwanted transitionsaccidentally created by phase etch process errors, produce similarvariations within the same detection spaces. Thus the application ofT-R, T-T″ and R-R″ detection space operations can be applied totransmitted and reflected APSM images to detect any phase features inthe photomask, but these operations are not sufficient to determinewhether a detected phase feature is a defect or a non-defective designtransition. However phase defects can be recognized by the system if alldetected phase features are properly compared and contrasted toreference photomask image data. Detected phase features can be evaluatedby die-to-die or die-to-database comparison to discriminate betweenphase defects and non-defective design features.

As noted above, APSM photomasks include phase shifting featuresresulting from depth differences within the glass or quartz portions ofthe specimen. The presence of edges due to depth differences can producescattering or other forms of image interference when the specimen isexposed to light. Due to these scattering and interference effects, APSMphase transitions can produce transmitted and reflected image variationssimilar to those shown in FIG. 28 during inspection. Specifically, phasetransitions on the clear substrate can produce signals similar to theblips 371, 375 and 377 shown in FIG. 18. Phase defects produced byerrors during the etch process can produce similar signals. The previoussystem identifies phase features using the image filters and referencemaps shown in FIGS. 19-24 in the same way particles can be identified asdefects. However, these operations are insufficient to determine whethera detected phase feature is actually a design transition or a defect.Therefore the previous system simply flags any detected phase feature asa defect and requires further processing or interpretation to properlyverify the feature as defective or non-defective.

The present system also identifies these phase features using the imagefilters and reference maps shown in FIGS. 19-24. Furthermore, thepresent system recognizes that a detected phase feature may not actuallybe a defect. The system interprets outputs of the image operations inFIGS. 19-24 are to be phase feature indicators rather than defectindicators, corresponding to the existence of a phase feature ratherthan a definite defect. To determine whether the feature corresponds toan actual defect, the system evaluates the output both with respect tothe reference image and by die-to-die or die to database comparison. Ifthe feature is not matched in the reference image, then the algorithmindicats the existence of a defect.

The present system, the system recognizes that a phase transition maynot actually be a defect. The output of the image operations in FIGS.19-24 are evaluated with respect to the reference image and interpretedto be phase transition indicators rather than defect indicators. In thisembodiment the output of the system corresponds to the existence of afeature rather than a definite defect. To determine whether the featurecorresponds to an actual defect, the output is evaluated by die-to-dieor die to database comparison. If the feature is not matched in thereference image, then the algorithm indicates the existence of a defect.

The present system detects APSM phase defects using transmitted andreflected image acquisition and processing. For this realization, thepreprocessing block 2704 in FIG. 27 b applies the image processingoperations shown in FIGS. 19-24 on both transmitted and reflected imagesto produce optical image features. Features having recognizable phasetransition characteristics, such as a known phase shift based on knownphotomask characteristics, are flagged. It is generally recognized thatparticular APSM specimens may have 45, 60, or 90 degree phase shifts, orother known angular shifts, and these shifts may be recognized as knowncharacteristics and flagged accordingly. These flagged features areencoded to indicate whether or not a phase transition was detected andpassed to subsequent processing locks.

Preprocessing block 2705 also applies a feature transform operation tothe reference or database image to produce reference phase features. Thefeature transform operation takes known information into account, suchas phase transition levels for the specimen, and transitionsrecognizable features into data representations thereof. The system thenperforms all subsequent functions on the optical and reference featuresincluding an align/interpolate/compare algorithm for detection of phasedefects. This subsequent processing is performed by the system in block2706.

Significant benefits exist for the present inspection system over theuse of transmitted images alone. In general, the use of both transmittedand reflected images produces a higher probability of phase transitiondetection than with the use of transmitted imaging alone. Use of bothimages also reduces the probability of false phase transition detectiondue to systematic noise. Also, in the case of APSM specimen inspection,the transmitted and reflected image components are phase shifted bydifferent intervals as a result of differences in optical data length.In some cases, depending on the actual phase shift induced on theilluminating light, a reflected image may produce a strongersignal-to-noise ratio than a transmitted image on APSM phasetransitions.

As noted, one particular aspect of the current invention is to extendthe application of transmitted and reflected imaging to photomaskpattern inspection with die-to-die or die-to-database comparisonmethods.

In evaluating the performance and capabilities of pattern inspectionsystems with various sample or pattern types, it became apparent thattransmitted optical image characteristics related to EPSM were not inconformance with other specimen types. In particular, the normalizedtransmitted scan plot shown in FIG. 17 is altered when inspecting anEPSM as a result of the phase shifting properties of the mask.Furthermore, interference due to phase shifting can reduce thetransmitted image response associated with small defects in thesubstrate, such as pinholes. Reflected images can be used to correct orenhance transmitted image deficiencies and abnormalities associated withpatterns and defects.

For the plot shown in FIG. 17, each value on the greyscale axis isassociated with a single point on the scan axis. This uniqueness betweengreyscale value and scan coordinate is no longer valid when scanning,for example, an EPSM sample. As shown in FIG. 26, the greyscale plot ofnormalized transmitted and reflected light signals for a scan across anEPSM substrate varies such that the transmitted greyscale values are notuniquely associated with a single coordinate on the scan axis, orequivalently, on the inspected substrate. This irregularity occursbecause the dark EPSM material is not completely opaque and passes lightthrough the specimen. It has been noted that for dark and clearmaterials, destructive interference occurs at pattern edges producing adark fringe in the transmitted image scan. Away from edges the scantransitions to the typical flat field response.

Therefore, a transmitted image scan across the substrate produces agreyscale plot as shown in FIG. 26, with a dark fringe at the patternedge that creates an ambiguity between greyscale values and scancoordinates, unlike the plots of FIGS. 17 and 18. This ambiguity isdetrimental to the defect detection process, producing errors duringpreprocessing, alignment, interpolation, and comparison.

The system addresses this transmission interference problem whenperforming the inspection and is illustrated in FIG. 28. Transmitted andreflected light intensities are passed through fiber optic channels tominimize propagation offsets and then sampled simultaneously. The systemsamples both transmitted and reflected images and passes them toremapping block 2801, which remaps each T-R sample to a single greyscalevalue, converting the two transmitted and reflected images into a singleimage.

The remap function is designed to produce images with no dark fringenear edges. The remap removes the fringe by reference to the reflectedgreyscale data, which is not altered by transmissive phase-shifting andis unambiguous in the scan region coincidental with the transmittedfringe, as shown in FIG. 26. While each point in the scan may notproduce a unique transmitted greyscale value, each point in the scandoes produce a unique point in the TR plane. The entire scan produces aset of points on a curve in TR-space as in FIG. 29, which represents thecorrelation between transmitted and reflected values on the substrate. Aunique remap value can be assigned to every point on the curve, and theremap will be unique for every point on the scan axis, containing nofringes.

The system performs a pattern inspection algorithm on the remapped imageto determine defects at and around the edges of the specimen pattern. Byremapping the transmitted and reflected images into a single image, theprocessing requirements for preprocessing, alignment, interpolation, andcomparison need not be duplicated for both images.

Further, in phase shift masks, a common problem is that a pinhole orphase variation may occur in the mask, which can e undetectable usingtransmitted light alone. In transmitted light, the modulation due to thepinhole may be very small due to destructive interference. Reflectedlight may, under some circumstances, produce a stronger signal thantransmitted light. By including the signal from reflected light, theremap tends to have increased variation and detectability resulting fromthe flaw. Sometimes this effect breaks the T-R correlation, and thusbecomes distinguishable by separating from the T-R plot, as in point2908 of FIG. 29.

The remap function is determined before inspection during a calibrationprocedure by evaluating samples of representative transmitted andreflected images. The calibration can be performed by various methods,where the common objective is to analyze the correlation betweentransmitted and reflected values and assign an appropriate relationshipbetween transmitted and reflected input values and remap output values.For any method, remap calibration must function effectively in thepresence of greyscale measurement noise.

For example, one calibration method identifies pattern edges within thesample images and constructs mean transmitted and reflected scanprofiles on a high-resolution grid along the scan axis. The remap scanprofile is also defined on the scan axis and directly assigned at eachpoint to the mean local transmitted and reflected values.

Another calibration method prepares a histogram in TR space representingthe distribution of transmitted and reflected image values on thesample. The distribution scatters about a curve in the TR plane thattraces the mean correlation between transmitted and reflected values asthe scan transitions across pattern edges. Density analysis can beapplied to the histogram to construct edge profiles and a TR correlationcurve, and assign remap values in TR space.

A common step in TR remap calibration is to determine a model for themean correlation curve in TR space and parameterize the curve with remapvalues. FIG. 26 shows the transmitted and reflected image profiles for ascan across a simple photomask pattern consisting of a single dark line.The mean correlation between transmitted and reflected image values isrepresented by curve 2902 in FIG. 29 Every coordinate on the scan axisin FIG. 26 corresponds to a particular point on curve 2902 in FIG. 29.As the scan transitions across the pattern in FIG. 26, the meantransmitted and reflected image values vary according to the meancorrelation curve between endpoints 2903 and 2904 in FIG. 29.

The remap function is defined by scan profile 2604 in FIG. 26. For eachpoint on the scan axis in FIG. 26, the transmitted, reflected, and remapvalues are uniquely correlated, providing a unique remap value for eachpoint on curve 2902 in FIG. 29. The variation of the remap function isrepresented by parametric increments along curve 2902. In particular,the minimum remap value occurs at endpoint 2903, and the maximum remapoccurs at endpoint 2904. At point 2905, which corresponds to point 2603of FIG. 26, the remap value is equal to the remap value 2605 at the samescan coordinate associated with point 2603.

To allow for noise fluctuations, points off the mean correlation curvemay be parameterized by selecting a point on the curve byminimum-distance rule. For contamination detection, TR space may befurther divided by boundary 2901 enclosing the mean correlation curve.Points outside boundary 2901 may be remapped by maximum distance rule toincrease the variation of contrast of dust and contaminants. The maximumdistance rule spits the region exterior to boundary 2901 into tworegions 2906 and 2907. After the entire TR plane is completelyparameterized, the remap function is then stored into remapping block2801 for reference during inspection.

Subsequent to the initial calibration procedure, the system scans thedesired specimen to inspect it for defects. As with the calibrationprocedure, transmitted and reflected light intensities are passedthrough fiber optic channels to minimize propagation offsets and thensampled simultaneously. The system samples both transmitted andreflected images and passes them to remapping block 2801, which remapseach T-R sample to a single greyscale value, converting the twotransmitted and reflected images into a single image. The systemperforms a pattern inspection algorithm on the remapped image todetermine defects at and around the edges of the specimen pattern.Errors in preprocessing, alignment, interpolation, and comparison stagesof the defect detection algorithm are reduced by removal of the fringe,enhancing the performance of the mask inspection. Furthermore, byselective dependence on both T&R image sources, the remap function canpass information from either image to the inspection algorithm forprocessing and algorithm for processing and defect detection, withoutduplicating the processing requirements for preprocessing, alignment,interpolation, and comparison for both images.

Reflected image data can be referenced to correct or enhance transmittedimage deficiencies and abnormalities associated with, for example, EPSMpatterns and defects, providing other possible benefits for EPSMinspection performance by remapping.

For example, interference due to phase-shifting can reduce thetransmitted image response associated with small defects in thesubstrate, such as pinholes and phase variations, and such a defect mayundetectable with transmitted imaging alone. However, the reflectedimage variation associated with the pinhole may be sufficient fordetection, so that the defect may be detectable with variations fromboth transmitted and reflected images transferred into the remap.

Other defects such as particles, films, and contaminants may break thecorrelation between transmitted and reflected values sufficiently tofall outside an envelope of probable TR values on the substrate. Thesedefects may be assigned remap values from the most distant neighbor onthe remap curve to enhance their contrast in the remapped image.

TR remap may thus be applied with systems incorporating die-to-die ordie-to-database comparison, or with comparison-independent systems whichinspect for particles, films; and contaminants. Extensions to the TRremap function are possible using the present invention. For example,the system may include filter networks as in FIG. 24 or neural networksas in FIG. 25. These networks may operate on transmitted and reflectedimage data to produce enhancements to remap image characteristics andimprove defect detection performance. One example is where both imagesmay be independently transformed into features before being remappedinto a single feature image. Another example is where both images areindependently passed through separate preprocessing, alignment,interpolation, and comparison operations, producing two defect imageswhich are then remapped into a single image.

While the system described herein is particularly useful in scanningEPSM and APSM, it is to be understood that the methods and informationmay be equally applicable and beneficial when scanning other types ofmasks, including chrome masks, and other wafer and reticle specimens.The procedures and structures used herein may therefore be beneficial invarious scanning environments.

Advanced Phase Shift

In addition to EPSM and APSM capabilites, the present system has theability to combine transmitted and reflected light and intelligentlyexamine the results based on known defect patterns to detect additionalanomalies present on the mask. The system employs an Advanced PhaseShift or APS method or algorithm to improve the system's ability todetect mask defects.

An example of a defect that is difficult to detect using transmittedlight is a 90 degree phase bump in a 180 degree shifter area. Thedifference in the phase defect signal between transmitted and reflectedlight signals results from a difference in the amount of phase shiftaffecting the two illumination modes. For the transmitted light signal,the phase shift angle can be calculated from the following equation:Φ_(t)=(2πD(n−1))/λ

For the reflected light signal, the phase shift angle can be calculatedfrom the equation:Φ_(r)=4πD/λ

-   -   where Φ_(t) is the phase shift angle of the transmitted light        signal (in radians), Φ_(r) is the phase shift angle of the        reflected light signal (in radians), D is the physical shifter        depth in the same units as the wavelength, π is the refractive        index of the shifter material, and λ is the wavelength of        illumination. Solving the two equations for D and setting the        results equal, the relationship between phase shift angles is as        follows:        Φ_(r)=2Φ_(t)/(n−1)

Assuming the refractive index of phase shifter material is approximately1.5, the reflected light phase shift angle is approximately 4 times thetransmitted light phase shift angle. An assumption of 1.5 for therefractive index of the shifter material is approximately equal to theindex of refraction at UV wavelengths of fused silica used for reticlesubstrates.

The maximum phase defect signal occurs at odd multiples of 180 degreephase shifts. Typical inspection system illumination wavelengths are onthe order of 364 nm, such as for the KLA-Tencor 365UV-HR. A 364 nmwavelength is longer than that used for DUV lithography, and thus phasedefects have proportionately decreased phase shifts and a decreasedtransmitted light signal for phase defects less than or equal to 180degrees. In 248 nm DUV lithography, the phased shift is decreased by afactor of approximately 0.68 at the inspection wavelength. The reflectedlight signal exhibits a phase shift of approximately 2.73 at theinspection wavelength.

The use of reflected light in combination with transmitted light canimprove detection of phase defects. The difficulty with using reflectedlight is managing image artifacts, such as the bright chrome halosresulting from the removal of the antireflective chrome layer duringquartz etching of phase shifters. Bright chrome halos may have variablewidths resulting from second write level registration tolerances withintra-plate variations. These variations are not observable when solelyusing transmitted light inspection techniques.

Several transmitted and reflected inspection implementations arepossible including independent inspections using either illuminationmode. As noted however, neither light mode may be ideal for phase-shiftmask inspection. In transmitted mode, the light signal from phasedefects may not be sufficiently strong for detection. In reflected mode,a light signal may contain bright chrome halo artifacts surrounding theetched quartz shifters, which can contribute to defect detection errorsfrom the system. To combine the benefits from both image sources,simultaneous transmitted and reflected (T&R) light signals are input toa lookup table (LUT), known as TR Map, which outputs an enhanced,remapped image signal S=S(T,R), which is a function of both transmittedand reflected inputs.

The remapped image S=S(T,R) is operated upon by an inspection algorithmto search for defects. The remap operation controls and transforms theflow of transmitted and reflected image information into the inspectionalgorithm, and allows the inspection algorithm to view selectedvariations from both inputs without modification or duplication ofprocessing requirements within the inspection algorithm itself.

The Advanced Phase Shift or APS algorithm can apply the TR Map functionto either die-to-die or die-to-database inspection modes. In die-to-dieinspection mode, two or more nominally identical die are compared, anddefects are indicated by differences between the die. A side view of asimplified representation of the system is presented in FIG. 30. FromFIG. 30, a scanning laser beam inputs light energy to the system bydirecting light energy to mask 3001. Reflected light energy is directedto lens 3002 and reflected light sensor 3003. Transmitted light passesto lens 3004, reticle 3005, lens 3006, and is received by transmittedlight sensor 3007. The transmitted light image received by thetransmitted light sensor 3007 and the reflected light image received bythe reflected light sensor 3003 are combined using a LUT and thecombined signal is compared using the die to die pattern APS algorithm.

In preparation for inspection, the system initially performs a lightcalibration function which captures T and R images with image rotationapplied and measures the extreme values from both T and R channels. Thesystem adjusts gains and offsets in both channels until the lightmeasurements match predetermined target and tolerance values.

After light calibration, the system performs a TR Map Calibrationprocedure to determine the TR Map function S=S(T,R). Again the systemcaptures T and R images with image rotation applied. (T,R) greyscalestatistics are analyzed in the (T,R) domain to derive TR Map. Aftercalibration, the system loads the TR Map function into a lookup table atthe input to the inspection algorithm and inspects the mask. By enablingthe system to inspect the remapped image S=S(T,R), TR Map broadlyexpands the inspection application capabilities of the system with thesimple addition of a lookup table at the input to the inspectionalgorithm.

As one example, TR Map can be applied to inspection of one type ofphase-shift photomask commonly known as a three-tone mask, composed ofquartz, chrome, and half-tone (embedded attenuating) phase-shiftmaterial. Three-tone masks introduce complex multi-range transmitted andreflected pattern signals which can cause algorithms designed for simplemask inspection to produce unwanted defect detection errors. Combinationof both transmitted and reflected signals by TR Map can resolve many ofthese signal complexities, by transforming or renormalizing differentinput greyscale ranges to a single common output range. For inspectionof three-tone masks, the benefits of TR Map to the inspection systeminclude the ability to detect defects in either transmitted or reflectedlight, the ability to remove edge fringes and other image anomaliesassociated with embedded phase-shift materials, and the ability tosimplify complex inputs for processing by an inspection algorithm.

As another example, low contrast patterns which fail to meet standardlight calibration objectives can be renormalized by TR Map operations torestore full greyscale range. This can have special significance forparticular types of inspections on low contrast patterns such ashalftone/chrome patterns, or ADI (After Development inspection)patterns, where the patterns may include photoresist in addition tochrome and/or quartz.

Furthermore, TR Map can be designed to enhance pattern inspection perapplication by remapping T&R greyscale values on certain photomaskfeatures. For APSM inspection, TR Map can produce remapped phase featuresignals on quartz known as Grey-on-Quartz (GOQ) signals. One majorapplication for GOQ is the amplification of phase defect signals byrenormalizing their reflected light components. For three-tone maskinspection, TR Map can produce remapped chrome feature signals on quartzknown as Grey-on-Chrome (GOC) signals. One major application for GOC isthe simultaneous inspection of all three pattern materials on athree-tone mask: chrome (CR), quartz (QZ), and half-tone (HT)phase-shift material.

Each TR Map species has a unique set of calibration parameters. Eachspecies defines a unique mathematical method for generating the TR Mapfunction S=S(T,R). Typically, S may generally be any analytical ormathematical model of a surface over a two-dimensional domain S=S(T,R)For example, S may be defined by any combination of analytical,morphological, or statistical transformations or filtering operationsperformed on the (T,R) sample histogram defined earlier. In thefollowing description of various TR Map species and calibration methods,the term “slug” refers to a histogram, in (T,R) space, of pattern signaldata gathered by the system, filled and smoothed by filteringoperations. Typically each species is designed with a set of variableparameters to permit a controllable range of output responses to inputvariations. Different applications within a species may requiredifferent optimal parameter values to be set based on other inspectionconditions.

TR_Map_APSM: Used for inspection of alternating PSM, oth dark-field andbright-field. Above the TR slug, the TR_Map_APSM depends on transmittedlight only, to be blind to reflected signals and chrome halo variations.The transmitted input can be scaled or shifted if necessary by a set ofpolynomial coefficients. This can be useful on certain APSMs having weakimage modulation due to deep quartz trenches.

Below the TR slug, APSM amplifies reflected variations on quartz forhigh sensitivity on phase edges and defects. This amplification isreferred as Grey-on-Quartz (GOQ), and the TR subspace where it appliesis referred to as the GOQ domain. Here the remap depends on bothtransmitted and reflected light, and the rate of GOQ variation onreflected light can be controlled by a set of polynomial coefficients.

TR_Map_TT1: Used for inspection of low-contrast HT/CR patterns. TT1 is avariation of APSM adapted for inspection of HT/CR patterns only. TT1renormalizes transmitted greyscale to an optimal range on HT/CR patternsfor maximum defect sensitivity. TT1 renormalization is controlled by aset of polynomial coefficients.

TR_Map_TT2: Used for inspection of 3-tone PSM with only HT/QZ and HT/CRpattern edges. TT2 remaps the HT/QZ boundary of the slug to optimizesensitivity on quartz patterns. Shifting, stretching, and shapingfunctions can be parametrically adjusted to control the profile of theremap on the HT/QZ boundary. Curve fitting procedures may then be usedto extrapolate the remap over all TR space. For example three paraboliccoefficients can be computed from three conditions. Remap curves can befit to the HT/QZ slug boundary, with a second condition to produce a GOCremap on chrome, and a third condition to define the curvature.

TR_Map_TT3: Used for inspection of 3-tone PSM with all pattern edgecombinations. There are three TT3 options or variations, depending onwhich two pattern edges are renormalized. One option remaps the HT/QZand CR/QZ boundaries of the slug to optimize sensitivity on quartzpattern edges. A second TT3 option remaps the HT/CR and CR/QZ boundariesof the slug to optimize sensitivity on chrome pattern edges. A third TT3option remaps the HT/CR and HT/QZ boundaries of the slug to optimizesensitivity on halftone pattern edges.

Shifting, stretching, and shaping functions can be parametricallyadjusted to control the profiles on the two remapped boundaries. Curvefitting procedures may then be used to extrapolate the remap over all TRspace. For example three parabolic coefficients can be computed fromthree conditions. Remap curves can be fit to the two remapped slugboundaries, with a third condition to define the curvature. Otherfunctions and parameters may be necessary to define GOC domains. Forexample, one type of GOC remap varies linearly with T between twoendpoints on the HT/CR slug boundary.

FIGS. 31 through 35 illustrate the various TR Map species describedabove. These remap (S) contours have been graphed to exhibit the generaldependence of each remap on T and R inputs. One TT3 example, illustratedin FIG. 34, has negative GOC slope, with respect to T. Another TT3example, illustrated in FIG. 35, has positive GOC slope. An alternativeto GOC for HT/CR inspection can be derived by fitting the S(T,R)polynomials to the HT/CR slug. FIGS. 36 and 37 illustrate two possiblevariations to this alternative TT3 approach. In FIG. 36, TT3renormalizes the HT/CR and HT/QZ slug boundaries. In FIG. 37, TT3renormalizes the HT/CR and CR/QZ slug boundaries. With these various TT3options, the system can be set up for three-tone mask inspection withoptimum sensitivity on any selected material.

TR_Map_T: Passes T and rejects R. Primarily for diagnostic purposes,although it can have useful applications including OPC (OpticalProximity Correction) inspection and second-level ADI.

TR_Map_R: Passes R and rejects T. Useful for ADI.

While the invention has been described in connection with specificembodiments thereof, it will be understood that the invention is capableof further modifications. This application is intended to cover anyvariations, uses or adaptations of the invention following, in general,the principles of the invention, and including such departures from thepresent disclosure as come within known and customary practice withinthe art to which the invention pertains.

1. A method for inspecting a specimen having at least one featurelocated thereon, comprising: performing a transmitted energy inspectionand a reflected energy inspection of said specimen; submitting resultsfrom said transmitted energy inspection and said reflected energyinspection to a transmitted-reflected look up table, wherein saidtransmitted-reflected look up table comprises transmitted and reflectedmodification parameters dependent upon predetermined characteristics ofsaid feature; and outputting modified combined transmitted energy dataand reflected energy data based on modified results determined by saidtransmitted-reflected look up table.
 2. The method of claim 1, whereinboundaries of said specimen are remapped to optimize sensitivity onedges located on said specimen having predetermined compositions.
 3. Themethod of claim 1, wherein said submitting step results in a transmittedrepresentation and a reflected representation, further comprisingapplying shifting, stretching, and shaping functions to at least one ofthe transmitted representation and the reflected representation.
 4. Themethod of claim 1, wherein said submitting step results in a transmittedrepresentation and a reflected representation, further comprisingnormalizing at least one of said transmitted representation and saidreflected representation.
 5. The method of claim 1, wherein saidsubmitting step results in a transmitted representation and a reflectedrepresentation, further comprising applying curve fitting to at leastone of said transmitted representation and said reflectedrepresentation.
 6. The method of claim 1, wherein said submitting stepresults in a transmitted representation and a reflected representation,further comprising selectively ignoring at least one of the transmittedrepresentation and the reflected representation.
 7. A method forinspecting a specimen, comprising: performing a transmitted energyinspection and a reflected energy inspection of said specimen, therebyproducing a transmitted representation and a reflected representation ofsaid specimen; and modifying at least one of the transmittedrepresentation and the reflected representation based on an expectedreceived transmitted/received signal dependent upon predeterminedknowledge associated with specimen composition.
 8. The method of claim7, wherein said modifying utilizes a transmitted-reflected look up tablehaving coefficients based upon known characteristics of previouslyinspected specimens.
 9. The method of claim 8, wherein boundaries ofsaid specimen are remapped to optimize sensitivity on edges havingpredetermined compositions.
 10. The method of claim 8, furthercomprising applying shifting, stretching, and shaping functions to atleast one of the transmitted representation and the reflectedrepresentation.
 11. The method of claim 8, further comprisingnormalizing at least one of said transmitted representation and saidreflected representation.
 12. The method of claim 8, further comprisingapplying curve fitting to at least one of said transmittedrepresentation and said reflected representation.
 13. The method ofclaim 8, further comprising selectively ignoring at least one of thetransmitted representation and the reflected representation.
 14. Amethod for inspecting a specimen, comprising: performing a transmittedenergy inspection and a reflected energy inspection of said specimen,thereby producing a transmitted representation and a reflectedrepresentation of said specimen; and modifying said transmittedrepresentation and said reflected representation based on predeterminedknown transmitted/reflected characteristics of previously inspectedspecimens having physical feature compositions similar to those of thespecimen.
 15. The method of claim 14, wherein said modifying utilizes atransmitted-reflected look up table having coefficients based upon knowncharacteristics of previously inspected specimens.
 16. The method ofclaim 14, wherein boundaries of said specimen are remapped to optimizesensitivity on edges having predetermined compositions.
 17. The methodof claim 14, further comprising applying shifting, stretching, andshaping functions to at least one of the transmitted representation andthe reflected representation.
 18. The method of claim 14, furthercomprising normalizing at least one of said transmitted representationand said reflected representation.
 19. The method of claim 14, furthercomprising applying curve fitting to at least one of said transmittedrepresentation and said reflected representation.
 20. The method ofclaim 14, further comprising selectively ignoring at least one of thetransmitted representation and the reflected representation.
 21. Amethod for inspecting a specimen, comprising: performing a calibrationfunction, capturing transmitted and reflected images and quantifyingextreme values for transmitted and reflected images; performing atransmitted-reflected map calibration function, producing atransmitted-reflected map function; loading the transmitted-reflectedmap function into a lookup table, producing a set of inspectionparameters; and inspecting the specimen based on the inspectionparameters.